polynomial_chaos
- pydantic model dakota.spec.method.polynomial_chaos.PceSelection
Generated model for PceSelection
Show JSON schema
{ "title": "PceSelection", "description": "Generated model for PceSelection", "type": "object", "properties": { "polynomial_chaos": { "$ref": "#/$defs/PceConfig", "x-aliases": [ "nond_polynomial_chaos" ], "x-materialization": [ { "ir_key": "method.algorithm", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "POLYNOMIAL_CHAOS" } ] } }, "$defs": { "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "ExpansionOptionsDiagCov": { "additionalProperties": false, "description": "Display only the diagonal terms of the covariance matrix", "properties": { "diagonal_covariance": { "const": true, "default": true, "description": "Display only the diagonal terms of the covariance matrix", "title": "Diagonal Covariance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.covariance_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIAGONAL_COVARIANCE" } ] } }, "title": "ExpansionOptionsDiagCov", "type": "object" }, "ExpansionOptionsDistributionComplementary": { "additionalProperties": false, "description": "Computes statistics according to complementary cumulative functions", "properties": { "complementary": { "const": true, "default": true, "description": "Computes statistics according to complementary cumulative functions", "title": "Complementary", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMPLEMENTARY" } ] } }, "title": "ExpansionOptionsDistributionComplementary", "type": "object" }, "ExpansionOptionsDistributionCumulative": { "additionalProperties": false, "description": "Computes statistics according to cumulative functions", "properties": { "cumulative": { "const": true, "default": true, "description": "Computes statistics according to cumulative functions", "title": "Cumulative", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CUMULATIVE" } ] } }, "title": "ExpansionOptionsDistributionCumulative", "type": "object" }, "ExpansionOptionsExportApproxPointsFile": { "additionalProperties": false, "description": "Output file for surrogate model value evaluations", "properties": { "filename": { "description": "Output file for surrogate model value evaluations", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.export_approx_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileCustomAnnotated" }, { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileAnnotated" }, { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "ExpansionOptionsExportApproxPointsFileAnnotated", "x-union-pattern": 1 } }, "required": [ "filename" ], "title": "ExpansionOptionsExportApproxPointsFile", "type": "object" }, "ExpansionOptionsExportApproxPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileAnnotated", "type": "object" }, "ExpansionOptionsExportApproxPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig" } }, "title": "ExpansionOptionsExportApproxPointsFileCustomAnnotated", "type": "object" }, "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig", "type": "object" }, "ExpansionOptionsExportApproxPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileFreeform", "type": "object" }, "ExpansionOptionsFinalMomentsCentral": { "additionalProperties": false, "description": "Output central moments and include them within the set of final statistics.", "properties": { "central": { "const": true, "default": true, "description": "Output central moments and include them within the set of final statistics.", "title": "Central", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CENTRAL_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsCentral", "type": "object" }, "ExpansionOptionsFinalMomentsNoneKeyword": { "additionalProperties": false, "description": "Omit moments from the set of final statistics.", "properties": { "none": { "const": true, "default": true, "description": "Omit moments from the set of final statistics.", "title": "None", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NO_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsNoneKeyword", "type": "object" }, "ExpansionOptionsFinalMomentsStandard": { "additionalProperties": false, "description": "Output standardized moments and include them within the set of final statistics.", "properties": { "standard": { "const": true, "default": true, "description": "Output standardized moments and include them within the set of final statistics.", "title": "Standard", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "STANDARD_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsStandard", "type": "object" }, "ExpansionOptionsFullCov": { "additionalProperties": false, "description": "Display the full covariance matrix", "properties": { "full_covariance": { "const": true, "default": true, "description": "Display the full covariance matrix", "title": "Full Covariance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.covariance_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "FULL_COVARIANCE" } ] } }, "title": "ExpansionOptionsFullCov", "type": "object" }, "ExpansionOptionsGenReliabilityLevels": { "additionalProperties": false, "description": "Specify generalized relability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify generalized relability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_gen_reliability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``gen_reliability_levels`` correspond to which response", "title": "Num Gen Reliability Levels" } }, "required": [ "values" ], "title": "ExpansionOptionsGenReliabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsgenreliabilitylevels", "validationErrorMessage": "For expansionoptionsgenreliabilitylevels, sum of num_gen_reliability_levels must equal length of values.", "validationFields": [ "num_gen_reliability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsProbabilityLevels": { "additionalProperties": false, "description": "Specify probability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify probability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_probability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``probability_levels`` correspond to which response", "title": "Num Probability Levels" } }, "required": [ "values" ], "title": "ExpansionOptionsProbabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsprobabilitylevels", "validationErrorMessage": "For expansionoptionsprobabilitylevels, all elements of values must be in [0, 1].", "validationFields": [ "values" ], "validationLiterals": [], "validationRuleName": "check_probability_list" }, { "validationContext": "expansionoptionsprobabilitylevels", "validationErrorMessage": "For expansionoptionsprobabilitylevels, sum of num_probability_levels must equal length of values.", "validationFields": [ "num_probability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsProbabilityRefinement": { "additionalProperties": false, "description": "Allow refinement of probability and generalized reliability results using importance sampling", "properties": { "importance_sampling_approach": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinementImportance" }, { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinementAdaptImport" }, { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinementMmAdaptImport" } ], "description": "Importance Sampling Approach", "title": "Importance Sampling Approach", "x-union-pattern": 4 }, "refinement_samples": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of samples used to refine a probability estimate or sampling design.", "title": "Refinement Samples", "x-materialization": [ { "ir_key": "method.nond.refinement_samples", "ir_value_type": "IntVector", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "importance_sampling_approach" ], "title": "ExpansionOptionsProbabilityRefinement", "type": "object" }, "ExpansionOptionsProbabilityRefinementAdaptImport": { "additionalProperties": false, "description": "Importance sampling option for probability refinement", "properties": { "adapt_import": { "const": true, "default": true, "description": "Importance sampling option for probability refinement", "title": "Adapt Import", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.integration_refinement", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "AIS" } ] } }, "title": "ExpansionOptionsProbabilityRefinementAdaptImport", "type": "object" }, "ExpansionOptionsProbabilityRefinementImportance": { "additionalProperties": false, "description": "Importance sampling option for probability refinement", "properties": { "importance": { "const": true, "default": true, "description": "Importance sampling option for probability refinement", "title": "Importance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.integration_refinement", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "IS" } ] } }, "title": "ExpansionOptionsProbabilityRefinementImportance", "type": "object" }, "ExpansionOptionsProbabilityRefinementMmAdaptImport": { "additionalProperties": false, "description": "Importance sampling option for probability refinement", "properties": { "mm_adapt_import": { "const": true, "default": true, "description": "Importance sampling option for probability refinement", "title": "Mm Adapt Import", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.integration_refinement", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "MMAIS" } ] } }, "title": "ExpansionOptionsProbabilityRefinementMmAdaptImport", "type": "object" }, "ExpansionOptionsReliabilityLevels": { "additionalProperties": false, "description": "Specify reliability levels at which the response values will be estimated", "properties": { "values": { "description": "Specify reliability levels at which the response values will be estimated", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_reliability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``reliability_levels`` correspond to which response", "title": "Num Reliability Levels" } }, "required": [ "values" ], "title": "ExpansionOptionsReliabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsreliabilitylevels", "validationErrorMessage": "For expansionoptionsreliabilitylevels, sum of num_reliability_levels must equal length of values.", "validationFields": [ "num_reliability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsResponseLevels": { "additionalProperties": false, "description": "Values at which to estimate desired statistics for each response", "properties": { "values": { "description": "Values at which to estimate desired statistics for each response", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_response_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of values at which to estimate desired statistics for each response", "title": "Num Response Levels" }, "compute": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevelsCompute" }, { "type": "null" } ], "default": null, "description": "Selection of statistics to compute at each response level" } }, "required": [ "values" ], "title": "ExpansionOptionsResponseLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsresponselevels", "validationErrorMessage": "For expansionoptionsresponselevels, sum of num_response_levels must equal length of values.", "validationFields": [ "num_response_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsResponseLevelsCompute": { "additionalProperties": false, "description": "Selection of statistics to compute at each response level", "properties": { "statistic": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeProbabilities" }, { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeReliabilities" }, { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeGenReliabilities" } ], "description": "Statistics to Compute", "title": "Statistic", "x-union-pattern": 4 }, "system": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeSystemSeries" }, { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeSystemParallel" }, { "type": "null" } ], "default": null, "description": "Compute system reliability (series or parallel)", "title": "System", "x-union-pattern": 2 } }, "required": [ "statistic" ], "title": "ExpansionOptionsResponseLevelsCompute", "type": "object" }, "ExpansionOptionsResponseLevelsComputeGenReliabilities": { "additionalProperties": false, "description": "Computes generalized reliabilities associated with response levels", "properties": { "gen_reliabilities": { "const": true, "default": true, "description": "Computes generalized reliabilities associated with response levels", "title": "Gen Reliabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "GEN_RELIABILITIES" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeGenReliabilities", "type": "object" }, "ExpansionOptionsResponseLevelsComputeProbabilities": { "additionalProperties": false, "description": "Computes probabilities associated with response levels", "properties": { "probabilities": { "const": true, "default": true, "description": "Computes probabilities associated with response levels", "title": "Probabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "PROBABILITIES" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeProbabilities", "type": "object" }, "ExpansionOptionsResponseLevelsComputeReliabilities": { "additionalProperties": false, "description": "Computes reliabilities associated with response levels", "properties": { "reliabilities": { "const": true, "default": true, "description": "Computes reliabilities associated with response levels", "title": "Reliabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RELIABILITIES" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeReliabilities", "type": "object" }, "ExpansionOptionsResponseLevelsComputeSystemParallel": { "additionalProperties": false, "description": "Aggregate response statistics assuming a parallel system", "properties": { "parallel": { "const": true, "default": true, "description": "Aggregate response statistics assuming a parallel system", "title": "Parallel", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target_reduce", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SYSTEM_PARALLEL" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeSystemParallel", "type": "object" }, "ExpansionOptionsResponseLevelsComputeSystemSeries": { "additionalProperties": false, "description": "Aggregate response statistics assuming a series system", "properties": { "series": { "const": true, "default": true, "description": "Aggregate response statistics assuming a series system", "title": "Series", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target_reduce", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SYSTEM_SERIES" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeSystemSeries", "type": "object" }, "ExpansionOptionsRngMt19937": { "additionalProperties": false, "description": "Generates random numbers using the Mersenne twister", "properties": { "mt19937": { "const": true, "default": true, "description": "Generates random numbers using the Mersenne twister", "title": "Mt19937", "type": "boolean", "x-materialization": [ { "ir_key": "method.random_number_generator", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "mt19937" } ] } }, "title": "ExpansionOptionsRngMt19937", "type": "object" }, "ExpansionOptionsRngRnum2": { "additionalProperties": false, "description": "Generates pseudo-random numbers using the Pecos package", "properties": { "rnum2": { "const": true, "default": true, "description": "Generates pseudo-random numbers using the Pecos package", "title": "Rnum2", "type": "boolean", "x-materialization": [ { "ir_key": "method.random_number_generator", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "rnum2" } ] } }, "title": "ExpansionOptionsRngRnum2", "type": "object" }, "ExpansionOptionsSampleTypeLhs": { "additionalProperties": false, "description": "Uses Latin Hypercube Sampling (LHS) to sample variables", "properties": { "lhs": { "const": true, "default": true, "description": "Uses Latin Hypercube Sampling (LHS) to sample variables", "title": "Lhs", "type": "boolean", "x-materialization": [ { "ir_key": "method.sample_type", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "SUBMETHOD_LHS" } ] } }, "title": "ExpansionOptionsSampleTypeLhs", "type": "object" }, "ExpansionOptionsSampleTypeRandom": { "additionalProperties": false, "description": "Uses purely random Monte Carlo sampling to sample variables", "properties": { "random": { "const": true, "default": true, "description": "Uses purely random Monte Carlo sampling to sample variables", "title": "Random", "type": "boolean", "x-materialization": [ { "ir_key": "method.sample_type", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "SUBMETHOD_RANDOM" } ] } }, "title": "ExpansionOptionsSampleTypeRandom", "type": "object" }, "ExpansionOptionsVarianceBasedDecomp": { "additionalProperties": false, "description": "Activates global sensitivity analysis based on decomposition of response variance into main, interaction, and total effects", "properties": { "interaction_order": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "Specify the maximum number of variables allowed in an interaction when reporting interaction metrics.", "title": "Interaction Order", "x-materialization": [ { "ir_key": "method.nond.vbd_interaction_order", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "drop_tolerance": { "default": -1.0, "description": "Suppresses output of sensitivity indices with values lower than this tolerance", "title": "Drop Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.vbd_drop_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOptionsVarianceBasedDecomp", "type": "object" }, "ExpansionOrderCollocPointsBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsBPDNConfig", "type": "object" }, "ExpansionOrderCollocPointsLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsLarsConfig", "type": "object" }, "ExpansionOrderCollocRatioBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "ExpansionOrderCollocRatioBP", "type": "object" }, "ExpansionOrderCollocRatioBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/ExpansionOrderCollocRatioBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "required": [ "basis_pursuit_denoising" ], "title": "ExpansionOrderCollocRatioBPDN", "type": "object" }, "ExpansionOrderCollocRatioBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { 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"additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileAnnotated", "type": "object" }, "PceExpansionOrderImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotated", "type": "object" }, "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceExpansionOrderImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileFreeform", "type": "object" }, "PceImportExpansionFile": { "additionalProperties": false, "description": "Build a Polynomial Chaos Expansion (PCE) by importing expansion coefficients and a corresponding multi-index from a file", "properties": { "import_expansion_file": { "description": "Build a Polynomial Chaos Expansion (PCE) by importing expansion coefficients and a corresponding multi-index from a file", "title": "Import Expansion File", "type": "string", "x-materialization": [ { "ir_key": "method.nond.import_expansion_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "import_expansion_file" ], "title": "PceImportExpansionFile", "type": "object" }, "PceOptionsAskey": { "additionalProperties": false, "description": "Select the standardized random variables (and associated basis polynomials) from the Askey family that best match the user-specified random variables.", "properties": { "askey": { "const": true, "default": true, "description": "Select the standardized random variables (and associated basis polynomials) from the Askey family that best match the user-specified random variables.", "title": "Askey", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.expansion_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ASKEY_U" } ] } }, "title": "PceOptionsAskey", "type": "object" }, "PceOptionsWiener": { "additionalProperties": false, "description": "Use standard normal random variables (along with Hermite orthogonal basis polynomials) when transforming to a standardized probability space.", "properties": { "wiener": { "const": true, "default": true, "description": "Use standard normal random variables (along with Hermite orthogonal basis polynomials) when transforming to a standardized probability space.", "title": "Wiener", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.expansion_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "STD_NORMAL_U" } ] } }, "title": "PceOptionsWiener", "type": "object" }, "PceOrthogLeastInterp": { "additionalProperties": false, "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "properties": { "orthogonal_least_interpolation": { "$ref": "#/$defs/PceOrthogLeastInterpConfig", "x-aliases": [ "least_interpolation", "oli" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_LEAST_INTERPOLATION" } ] } }, "required": [ "orthogonal_least_interpolation" ], "title": "PceOrthogLeastInterp", "type": "object" }, "PceOrthogLeastInterpConfig": { "additionalProperties": false, "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "properties": { "collocation_points": { "description": "Number of collocation points used to estimate expansion coefficients", "title": "Collocation Points", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.collocation_points", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "tensor_grid": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.", "title": "Tensor Grid", "x-materialization": [ { "ir_key": "method.nond.tensor_grid_order", "ir_value_type": "UShortArray", "storage_type": "DIRECT_VALUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] }, "import_build_points_file": { "anyOf": [ { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] } }, "required": [ "collocation_points" ], "title": "PceOrthogLeastInterpConfig", "type": "object", "x-model-validations": [ { "validationContext": "pceorthogleastinterpconfig", "validationErrorMessage": "For pceorthogleastinterpconfig, all elements of tensor_grid must be >= 0.", "validationFields": [ "tensor_grid" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "PceOrthogLeastInterpImportBuildPointsFile": { "additionalProperties": false, "description": "File containing points you wish to use to build a surrogate", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "PceOrthogLeastInterpImportBuildPointsFile", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileFreeform", "type": "object" }, "PceQuadratureOrder": { "additionalProperties": false, "description": "Order for tensor-products of Gaussian quadrature rules", "properties": { "quadrature_order": { "$ref": "#/$defs/PceQuadratureOrderConfig", "argument": "order" } }, "required": [ "quadrature_order" ], "title": "PceQuadratureOrder", "type": "object" }, "PceQuadratureOrderConfig": { "additionalProperties": false, "description": "Order for tensor-products of Gaussian quadrature rules", "properties": { "order": { "default": 65535, "description": "Order for tensor-products of Gaussian quadrature rules", "title": "Order", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.quadrature_order", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "dimension_preference": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "A set of weights specifying the realtive importance of each uncertain variable (dimension)", "title": "Dimension Preference", "x-materialization": [ { "ir_key": "method.nond.dimension_preference", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "nesting_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceQuadratureOrderNested" }, { "$ref": "#/$defs/PceQuadratureOrderNonNested" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Nesting", "title": "Nesting Rule", "x-union-pattern": 2 } }, "title": "PceQuadratureOrderConfig", "type": "object" }, "PceQuadratureOrderNested": { "additionalProperties": false, "description": "Enforce use of nested quadrature rules if available", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "title": "PceQuadratureOrderNested", "type": "object" }, "PceQuadratureOrderNonNested": { "additionalProperties": false, "description": "Enforce use of non-nested quadrature rules", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "title": "PceQuadratureOrderNonNested", "type": "object" }, "PceRefinementPRefinementDimAdaptive": { "additionalProperties": false, "description": "Perform anisotropic expansion refinement by preferentially adapting in dimensions that are detected to have higher \\\"importance\\\".", "properties": { "dimension_adaptive": { "anyOf": [ { "$ref": "#/$defs/PceRefinementPRefinementDimAdaptiveSobol" }, { "$ref": "#/$defs/PceRefinementPRefinementDimAdaptiveDecay" }, { "$ref": "#/$defs/PceRefinementPRefinementDimAdaptiveGeneralized" } ], "description": "Perform anisotropic expansion refinement by preferentially adapting in dimensions that are detected to have higher \"importance\".", "title": "Dimension Adaptive" } }, "required": [ "dimension_adaptive" ], "title": "PceRefinementPRefinementDimAdaptive", "type": "object" }, "PceRefinementPRefinementDimAdaptiveDecay": { "additionalProperties": false, "description": "Estimate spectral coefficient decay rates to guide dimension-adaptive refinement.", "properties": { "decay": { "const": true, "default": true, "description": "Estimate spectral coefficient decay rates to guide dimension-adaptive refinement.", "title": "Decay", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIMENSION_ADAPTIVE_CONTROL_DECAY" } ] } }, "title": "PceRefinementPRefinementDimAdaptiveDecay", "type": "object" }, "PceRefinementPRefinementDimAdaptiveGeneralized": { "additionalProperties": false, "description": "Use the generalized sparse grid dimension adaptive algorithm to refine a sparse grid approximation of stochastic expansion.", "properties": { "generalized": { "const": true, "default": true, "description": "Use the generalized sparse grid dimension adaptive algorithm to refine a sparse grid approximation of stochastic expansion.", "title": "Generalized", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIMENSION_ADAPTIVE_CONTROL_GENERALIZED" } ] } }, "title": "PceRefinementPRefinementDimAdaptiveGeneralized", "type": "object" }, "PceRefinementPRefinementDimAdaptiveSobol": { "additionalProperties": false, "description": "Estimate dimension preference for automated refinement of stochastic expansion using total Sobol' sensitivity indices.", "properties": { "sobol": { "const": true, "default": true, "description": "Estimate dimension preference for automated refinement of stochastic expansion using total Sobol' sensitivity indices.", "title": "Sobol", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIMENSION_ADAPTIVE_CONTROL_SOBOL" } ] } }, "title": "PceRefinementPRefinementDimAdaptiveSobol", "type": "object" }, "PceRefinementPRefinementUniform": { "additionalProperties": false, "description": "Refine an expansion uniformly in all dimensions.", "properties": { "uniform": { "const": true, "default": true, "description": "Refine an expansion uniformly in all dimensions.", "title": "Uniform", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "UNIFORM_CONTROL" } ] } }, "title": "PceRefinementPRefinementUniform", "type": "object" }, "PceSGLevel": { "additionalProperties": false, "description": "Level to use in sparse grid integration or interpolation", "properties": { "sparse_grid_level": { "$ref": "#/$defs/PceSGLevelConfig", "argument": "level" } }, "required": [ "sparse_grid_level" ], "title": "PceSGLevel", "type": "object" }, "PceSGLevelConfig": { "additionalProperties": false, "description": "Level to use in sparse grid integration or interpolation", "properties": { "level": { "default": 65535, "description": "Level to use in sparse grid integration or interpolation", "title": "Level", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.sparse_grid_level", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "dimension_preference": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "A set of weights specifying the realtive importance of each uncertain variable (dimension)", "title": "Dimension Preference", "x-materialization": [ { "ir_key": "method.nond.dimension_preference", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "growth_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceSGLevelRestricted" }, { "$ref": "#/$defs/PceSGLevelUnrestricted" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Growth", "title": "Growth Rule", "x-union-pattern": 2 }, "nesting_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceSGLevelNested" }, { "$ref": "#/$defs/PceSGLevelNonNested" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Nesting", "title": "Nesting Rule", "x-union-pattern": 2 } }, "title": "PceSGLevelConfig", "type": "object" }, "PceSGLevelNested": { "additionalProperties": false, "description": "Enforce use of nested quadrature rules if available", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "title": "PceSGLevelNested", "type": "object" }, "PceSGLevelNonNested": { "additionalProperties": false, "description": "Enforce use of non-nested quadrature rules", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "title": "PceSGLevelNonNested", "type": "object" }, "PceSGLevelRestricted": { "additionalProperties": false, "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "properties": { "restricted": { "const": true, "default": true, "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "title": "Restricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RESTRICTED" } ] } }, "title": "PceSGLevelRestricted", "type": "object" }, "PceSGLevelUnrestricted": { "additionalProperties": false, "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "properties": { "unrestricted": { "const": true, "default": true, "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "title": "Unrestricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "UNRESTRICTED" } ] } }, "title": "PceSGLevelUnrestricted", "type": "object" }, "Quiet": { "additionalProperties": false, "description": "Level 2 of 5 - less than normal", "properties": { "quiet": { "const": true, "default": true, "description": "Level 2 of 5 - less than normal", "title": "Quiet", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "QUIET_OUTPUT" } ] } }, "title": "Quiet", "type": "object" }, "RefinementMetricCov": { "additionalProperties": false, "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.", "properties": { "covariance": { "const": true, "default": true, "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.", "title": "Covariance", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_metric", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COVARIANCE_METRIC" } ] } }, "title": "RefinementMetricCov", "type": "object" }, "Silent": { "additionalProperties": false, "description": "Level 1 of 5 - minimum", "properties": { "silent": { "const": true, "default": true, "description": "Level 1 of 5 - minimum", "title": "Silent", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SILENT_OUTPUT" } ] } }, "title": "Silent", "type": "object" }, "Verbose": { "additionalProperties": false, "description": "Level 4 of 5 - more than normal", "properties": { "verbose": { "const": true, "default": true, "description": "Level 4 of 5 - more than normal", "title": "Verbose", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VERBOSE_OUTPUT" } ] } }, "title": "Verbose", "type": "object" } }, "additionalProperties": false, "required": [ "polynomial_chaos" ] }
- classmethod get_registry() dict[str, type[MethodSelection]]
Get registry, performing deferred registration on first call
- classmethod get_union()
Generate Union from all registered selections
- pydantic model dakota.spec.method.polynomial_chaos.PceConfig
Uncertainty quantification using polynomial chaos expansions
Show JSON schema
{ "title": "PceConfig", "description": "Uncertainty quantification using polynomial chaos expansions", "type": "object", "properties": { "model_pointer": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Identifier for model block to be used by a method", "title": "Model Pointer", "x-block-pointer": "model", "x-materialization": [ { "ir_key": "method.model_pointer", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "seed": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "Seed of the random number generator", "title": "Seed", "x-materialization": [ { "ir_key": "method.random_seed", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "fixed_seed": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Reuses the same seed value for multiple random sampling sets", "title": "Fixed Seed", "x-materialization": [ { "ir_key": "method.fixed_seed", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "samples_on_emulator": { "default": 0, "description": "Number of samples at which to evaluate an emulator (surrogate)", "title": "Samples On Emulator", "type": "integer", "x-aliases": [ "samples" ], "x-materialization": [ { "ir_key": "method.nond.samples_on_emulator", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "sample_type": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsSampleTypeLhs" }, { "$ref": "#/$defs/ExpansionOptionsSampleTypeRandom" }, { "type": "null" } ], "default": null, "description": "Selection of sampling strategy", "title": "Sample Type", "x-union-pattern": 2 }, "rng": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsRngMt19937" }, { "$ref": "#/$defs/ExpansionOptionsRngRnum2" } ], "description": "Selection of a random number generator", "title": "Rng", "x-model-default": "ExpansionOptionsRngMt19937", "x-union-pattern": 1 }, "probability_refinement": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinement" }, { "type": "null" } ], "default": null, "description": "Allow refinement of probability and generalized reliability results using importance sampling", "x-aliases": [ "sample_refinement" ] }, "final_moments": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsFinalMomentsNoneKeyword" }, { "$ref": "#/$defs/ExpansionOptionsFinalMomentsStandard" }, { "$ref": "#/$defs/ExpansionOptionsFinalMomentsCentral" } ], "description": "Output moments of the specified type and include them within the set of final statistics.", "title": "Final Moments", "x-model-default": "ExpansionOptionsFinalMomentsStandard", "x-union-pattern": 1 }, "response_levels": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Values at which to estimate desired statistics for each response", "x-materialization": [ { "ir_key": "method.nond.response_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "probability_levels": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsProbabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify probability levels at which to estimate the corresponding response value", "x-materialization": [ { "ir_key": "method.nond.probability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "reliability_levels": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsReliabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify reliability levels at which the response values will be estimated", "x-materialization": [ { "ir_key": "method.nond.reliability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "gen_reliability_levels": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsGenReliabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify generalized relability levels at which to estimate the corresponding response value", "x-materialization": [ { "ir_key": "method.nond.gen_reliability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "distribution": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsDistributionCumulative" }, { "$ref": "#/$defs/ExpansionOptionsDistributionComplementary" } ], "description": "Selection of cumulative or complementary cumulative functions", "title": "Distribution", "x-model-default": "ExpansionOptionsDistributionCumulative", "x-union-pattern": 1 }, "variance_based_decomp": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsVarianceBasedDecomp" }, { "type": "null" } ], "default": null, "description": "Activates global sensitivity analysis based on decomposition of response variance into main, interaction, and total effects", "x-materialization": [ { "ir_key": "method.variance_based_decomp", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "import_approx_points_file": { "anyOf": [ { "$ref": "#/$defs/ImportApproxPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "Filename for points at which to evaluate the PCE/SC surrogate" }, "export_approx_points_file": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "Output file for surrogate model value evaluations", "x-aliases": [ "export_points_file" ] }, "covariance_type": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsDiagCov" }, { "$ref": "#/$defs/ExpansionOptionsFullCov" }, { "type": "null" } ], "default": null, "description": "Covariance Type", "title": "Covariance Type", "x-union-pattern": 2 }, "normalized": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "The normalized specification requests output of PCE coefficients that correspond to normalized orthogonal basis polynomials", "title": "Normalized", "x-materialization": [ { "ir_key": "method.nond.normalized", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "export_expansion_file": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Export the coefficients and multi-index of a Polynomial Chaos Expansion (PCE) to a file", "title": "Export Expansion File", "x-materialization": [ { "ir_key": "method.nond.export_expansion_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "basis_family": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceOptionsAskey" }, { "$ref": "#/$defs/PceOptionsWiener" }, { "type": "null" } ], "default": null, "description": "Basis Polynomial Family", "title": "Basis Family", "x-union-pattern": 2 }, "refinement_metric": { "anyOf": [ { "$ref": "#/$defs/LevelMappings" }, { "$ref": "#/$defs/RefinementMetricCov" }, { "type": "null" } ], "default": null, "description": "Metric used for guiding adaptive refinement during UQ.", "title": "Refinement Metric", "x-union-pattern": 2 }, "convergence_tolerance": { "anyOf": [ { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext2ConvergenceTol" }, { "type": "null" } ], "argument": "value", "default": null, "description": "Stopping criterion based on objective function or statistics convergence" }, "p_refinement": { "anyOf": [ { "$ref": "#/$defs/PceRefinementPRefinementUniform" }, { "$ref": "#/$defs/PceRefinementPRefinementDimAdaptive" }, { "type": "null" } ], "default": null, "description": "Automatic polynomial order refinement", "title": "P Refinement", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "P_REFINEMENT" } ], "x-union-pattern": 2 }, "max_refinement_iterations": { "default": 9223372036854775807, "description": "Maximum number of expansion refinement iterations", "minimum": 0, "title": "Max Refinement Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.max_refinement_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "id_method": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Name the method block; helpful when there are multiple", "title": "Id Method", "x-materialization": [ { "ir_key": "method.id", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "output": { "anyOf": [ { "$ref": "#/$defs/Debug" }, { "$ref": "#/$defs/Verbose" }, { "$ref": "#/$defs/Normal" }, { "$ref": "#/$defs/Quiet" }, { "$ref": "#/$defs/Silent" } ], "description": "Control how much method information is written to the screen and output file", "title": "Output", "x-model-default": "Normal", "x-union-pattern": 1 }, "final_solutions": { "default": 0, "description": "Number of designs returned as the best solutions", "minimum": 0, "title": "Final Solutions", "type": "integer", "x-materialization": [ { "ir_key": "method.final_solutions", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "coefficient_approach": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceQuadratureOrder" }, { "$ref": "#/$defs/PceSGLevel" }, { "$ref": "#/$defs/PceCubatureIntegrand" }, { "$ref": "#/$defs/PceExpansionOrder" }, { "$ref": "#/$defs/PceOrthogLeastInterp" }, { "$ref": "#/$defs/PceImportExpansionFile" } ], "description": "Chaos coefficient estimation approach", "title": "Coefficient Approach", "x-union-pattern": 4 } }, "$defs": { "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "ExpansionOptionsDiagCov": { "additionalProperties": false, "description": "Display only the diagonal terms of the covariance matrix", "properties": { "diagonal_covariance": { "const": true, "default": true, "description": "Display only the diagonal terms of the covariance matrix", "title": "Diagonal Covariance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.covariance_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIAGONAL_COVARIANCE" } ] } }, "title": "ExpansionOptionsDiagCov", "type": "object" }, "ExpansionOptionsDistributionComplementary": { "additionalProperties": false, "description": "Computes statistics according to complementary cumulative functions", "properties": { "complementary": { "const": true, "default": true, "description": "Computes statistics according to complementary cumulative functions", "title": "Complementary", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMPLEMENTARY" } ] } }, "title": "ExpansionOptionsDistributionComplementary", "type": "object" }, "ExpansionOptionsDistributionCumulative": { "additionalProperties": false, "description": "Computes statistics according to cumulative functions", "properties": { "cumulative": { "const": true, "default": true, "description": "Computes statistics according to cumulative functions", "title": "Cumulative", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CUMULATIVE" } ] } }, "title": "ExpansionOptionsDistributionCumulative", "type": "object" }, "ExpansionOptionsExportApproxPointsFile": { "additionalProperties": false, "description": "Output file for surrogate model value evaluations", "properties": { "filename": { "description": "Output file for surrogate model value evaluations", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.export_approx_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileCustomAnnotated" }, { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileAnnotated" }, { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "ExpansionOptionsExportApproxPointsFileAnnotated", "x-union-pattern": 1 } }, "required": [ "filename" ], "title": "ExpansionOptionsExportApproxPointsFile", "type": "object" }, "ExpansionOptionsExportApproxPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileAnnotated", "type": "object" }, "ExpansionOptionsExportApproxPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig" } }, "title": "ExpansionOptionsExportApproxPointsFileCustomAnnotated", "type": "object" }, "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig", "type": "object" }, "ExpansionOptionsExportApproxPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileFreeform", "type": "object" }, "ExpansionOptionsFinalMomentsCentral": { "additionalProperties": false, "description": "Output central moments and include them within the set of final statistics.", "properties": { "central": { "const": true, "default": true, "description": "Output central moments and include them within the set of final statistics.", "title": "Central", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CENTRAL_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsCentral", "type": "object" }, "ExpansionOptionsFinalMomentsNoneKeyword": { "additionalProperties": false, "description": "Omit moments from the set of final statistics.", "properties": { "none": { "const": true, "default": true, "description": "Omit moments from the set of final statistics.", "title": "None", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NO_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsNoneKeyword", "type": "object" }, "ExpansionOptionsFinalMomentsStandard": { "additionalProperties": false, "description": "Output standardized moments and include them within the set of final statistics.", "properties": { "standard": { "const": true, "default": true, "description": "Output standardized moments and include them within the set of final statistics.", "title": "Standard", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "STANDARD_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsStandard", "type": "object" }, "ExpansionOptionsFullCov": { "additionalProperties": false, "description": "Display the full covariance matrix", "properties": { "full_covariance": { "const": true, "default": true, "description": "Display the full covariance matrix", "title": "Full Covariance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.covariance_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "FULL_COVARIANCE" } ] } }, "title": "ExpansionOptionsFullCov", "type": "object" }, "ExpansionOptionsGenReliabilityLevels": { "additionalProperties": false, "description": "Specify generalized relability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify generalized relability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_gen_reliability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``gen_reliability_levels`` correspond to which response", "title": "Num Gen Reliability Levels" } }, "required": [ "values" ], "title": "ExpansionOptionsGenReliabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsgenreliabilitylevels", "validationErrorMessage": "For expansionoptionsgenreliabilitylevels, sum of num_gen_reliability_levels must equal length of values.", "validationFields": [ "num_gen_reliability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsProbabilityLevels": { "additionalProperties": false, "description": "Specify probability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify probability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_probability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``probability_levels`` correspond to which response", "title": "Num Probability Levels" } }, "required": [ "values" ], "title": "ExpansionOptionsProbabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsprobabilitylevels", "validationErrorMessage": "For expansionoptionsprobabilitylevels, all elements of values must be in [0, 1].", "validationFields": [ "values" ], "validationLiterals": [], "validationRuleName": "check_probability_list" }, { "validationContext": "expansionoptionsprobabilitylevels", "validationErrorMessage": "For expansionoptionsprobabilitylevels, sum of num_probability_levels must equal length of values.", "validationFields": [ "num_probability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsProbabilityRefinement": { "additionalProperties": false, "description": "Allow refinement of probability and generalized reliability results using importance sampling", "properties": { "importance_sampling_approach": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinementImportance" }, { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinementAdaptImport" }, { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinementMmAdaptImport" } ], "description": "Importance Sampling Approach", "title": "Importance Sampling Approach", "x-union-pattern": 4 }, "refinement_samples": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of samples used to refine a probability estimate or sampling design.", "title": "Refinement Samples", "x-materialization": [ { "ir_key": "method.nond.refinement_samples", "ir_value_type": "IntVector", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "importance_sampling_approach" ], "title": "ExpansionOptionsProbabilityRefinement", "type": "object" }, "ExpansionOptionsProbabilityRefinementAdaptImport": { "additionalProperties": false, "description": "Importance sampling option for probability refinement", "properties": { "adapt_import": { "const": true, "default": true, "description": "Importance sampling option for probability refinement", "title": "Adapt Import", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.integration_refinement", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "AIS" } ] } }, "title": "ExpansionOptionsProbabilityRefinementAdaptImport", "type": "object" }, "ExpansionOptionsProbabilityRefinementImportance": { "additionalProperties": false, "description": "Importance sampling option for probability refinement", "properties": { "importance": { "const": true, "default": true, "description": "Importance sampling option for probability refinement", "title": "Importance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.integration_refinement", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "IS" } ] } }, "title": "ExpansionOptionsProbabilityRefinementImportance", "type": "object" }, "ExpansionOptionsProbabilityRefinementMmAdaptImport": { "additionalProperties": false, "description": "Importance sampling option for probability refinement", "properties": { "mm_adapt_import": { "const": true, "default": true, "description": "Importance sampling option for probability refinement", "title": "Mm Adapt Import", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.integration_refinement", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "MMAIS" } ] } }, "title": "ExpansionOptionsProbabilityRefinementMmAdaptImport", "type": "object" }, "ExpansionOptionsReliabilityLevels": { "additionalProperties": false, "description": "Specify reliability levels at which the response values will be estimated", "properties": { "values": { "description": "Specify reliability levels at which the response values will be estimated", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_reliability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``reliability_levels`` correspond to which response", "title": "Num Reliability Levels" } }, "required": [ "values" ], "title": "ExpansionOptionsReliabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsreliabilitylevels", "validationErrorMessage": "For expansionoptionsreliabilitylevels, sum of num_reliability_levels must equal length of values.", "validationFields": [ "num_reliability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsResponseLevels": { "additionalProperties": false, "description": "Values at which to estimate desired statistics for each response", "properties": { "values": { "description": "Values at which to estimate desired statistics for each response", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_response_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of values at which to estimate desired statistics for each response", "title": "Num Response Levels" }, "compute": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevelsCompute" }, { "type": "null" } ], "default": null, "description": "Selection of statistics to compute at each response level" } }, "required": [ "values" ], "title": "ExpansionOptionsResponseLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsresponselevels", "validationErrorMessage": "For expansionoptionsresponselevels, sum of num_response_levels must equal length of values.", "validationFields": [ "num_response_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsResponseLevelsCompute": { "additionalProperties": false, "description": "Selection of statistics to compute at each response level", "properties": { "statistic": { 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"PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceExpansionOrderImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileFreeform", "type": "object" }, "PceImportExpansionFile": { "additionalProperties": false, "description": "Build a Polynomial Chaos Expansion (PCE) by importing expansion coefficients and a corresponding multi-index from a file", "properties": { "import_expansion_file": { "description": "Build a Polynomial Chaos Expansion (PCE) by importing expansion coefficients and a corresponding multi-index from a file", "title": "Import Expansion File", "type": "string", "x-materialization": [ { "ir_key": "method.nond.import_expansion_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "import_expansion_file" ], "title": "PceImportExpansionFile", "type": "object" }, "PceOptionsAskey": { "additionalProperties": false, "description": "Select the standardized random variables (and associated basis polynomials) from the Askey family that best match the user-specified random variables.", "properties": { "askey": { "const": true, "default": true, "description": "Select the standardized random variables (and associated basis polynomials) from the Askey family that best match the user-specified random variables.", "title": "Askey", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.expansion_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ASKEY_U" } ] } }, "title": "PceOptionsAskey", "type": "object" }, "PceOptionsWiener": { "additionalProperties": false, "description": "Use standard normal random variables (along with Hermite orthogonal basis polynomials) when transforming to a standardized probability space.", "properties": { "wiener": { "const": true, "default": true, "description": "Use standard normal random variables (along with Hermite orthogonal basis polynomials) when transforming to a standardized probability space.", "title": "Wiener", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.expansion_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "STD_NORMAL_U" } ] } }, "title": "PceOptionsWiener", "type": "object" }, "PceOrthogLeastInterp": { "additionalProperties": false, "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "properties": { "orthogonal_least_interpolation": { "$ref": "#/$defs/PceOrthogLeastInterpConfig", "x-aliases": [ "least_interpolation", "oli" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_LEAST_INTERPOLATION" } ] } }, "required": [ "orthogonal_least_interpolation" ], "title": "PceOrthogLeastInterp", "type": "object" }, "PceOrthogLeastInterpConfig": { "additionalProperties": false, "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "properties": { "collocation_points": { "description": "Number of collocation points used to estimate expansion coefficients", "title": "Collocation Points", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.collocation_points", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "tensor_grid": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.", "title": "Tensor Grid", "x-materialization": [ { "ir_key": "method.nond.tensor_grid_order", "ir_value_type": "UShortArray", "storage_type": "DIRECT_VALUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] }, "import_build_points_file": { "anyOf": [ { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] } }, "required": [ "collocation_points" ], "title": "PceOrthogLeastInterpConfig", "type": "object", "x-model-validations": [ { "validationContext": "pceorthogleastinterpconfig", "validationErrorMessage": "For pceorthogleastinterpconfig, all elements of tensor_grid must be >= 0.", "validationFields": [ "tensor_grid" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "PceOrthogLeastInterpImportBuildPointsFile": { "additionalProperties": false, "description": "File containing points you wish to use to build a surrogate", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "PceOrthogLeastInterpImportBuildPointsFile", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileFreeform", "type": "object" }, "PceQuadratureOrder": { "additionalProperties": false, "description": "Order for tensor-products of Gaussian quadrature rules", "properties": { "quadrature_order": { "$ref": "#/$defs/PceQuadratureOrderConfig", "argument": "order" } }, "required": [ "quadrature_order" ], "title": "PceQuadratureOrder", "type": "object" }, "PceQuadratureOrderConfig": { "additionalProperties": false, "description": "Order for tensor-products of Gaussian quadrature rules", "properties": { "order": { "default": 65535, "description": "Order for tensor-products of Gaussian quadrature rules", "title": "Order", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.quadrature_order", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "dimension_preference": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "A set of weights specifying the realtive importance of each uncertain variable (dimension)", "title": "Dimension Preference", "x-materialization": [ { "ir_key": "method.nond.dimension_preference", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "nesting_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceQuadratureOrderNested" }, { "$ref": "#/$defs/PceQuadratureOrderNonNested" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Nesting", "title": "Nesting Rule", "x-union-pattern": 2 } }, "title": "PceQuadratureOrderConfig", "type": "object" }, "PceQuadratureOrderNested": { "additionalProperties": false, "description": "Enforce use of nested quadrature rules if available", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "title": "PceQuadratureOrderNested", "type": "object" }, "PceQuadratureOrderNonNested": { "additionalProperties": false, "description": "Enforce use of non-nested quadrature rules", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "title": "PceQuadratureOrderNonNested", "type": "object" }, "PceRefinementPRefinementDimAdaptive": { "additionalProperties": false, "description": "Perform anisotropic expansion refinement by preferentially adapting in dimensions that are detected to have higher \\\"importance\\\".", "properties": { "dimension_adaptive": { "anyOf": [ { "$ref": "#/$defs/PceRefinementPRefinementDimAdaptiveSobol" }, { "$ref": "#/$defs/PceRefinementPRefinementDimAdaptiveDecay" }, { "$ref": "#/$defs/PceRefinementPRefinementDimAdaptiveGeneralized" } ], "description": "Perform anisotropic expansion refinement by preferentially adapting in dimensions that are detected to have higher \"importance\".", "title": "Dimension Adaptive" } }, "required": [ "dimension_adaptive" ], "title": "PceRefinementPRefinementDimAdaptive", "type": "object" }, "PceRefinementPRefinementDimAdaptiveDecay": { "additionalProperties": false, "description": "Estimate spectral coefficient decay rates to guide dimension-adaptive refinement.", "properties": { "decay": { "const": true, "default": true, "description": "Estimate spectral coefficient decay rates to guide dimension-adaptive refinement.", "title": "Decay", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIMENSION_ADAPTIVE_CONTROL_DECAY" } ] } }, "title": "PceRefinementPRefinementDimAdaptiveDecay", "type": "object" }, "PceRefinementPRefinementDimAdaptiveGeneralized": { "additionalProperties": false, "description": "Use the generalized sparse grid dimension adaptive algorithm to refine a sparse grid approximation of stochastic expansion.", "properties": { "generalized": { "const": true, "default": true, "description": "Use the generalized sparse grid dimension adaptive algorithm to refine a sparse grid approximation of stochastic expansion.", "title": "Generalized", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIMENSION_ADAPTIVE_CONTROL_GENERALIZED" } ] } }, "title": "PceRefinementPRefinementDimAdaptiveGeneralized", "type": "object" }, "PceRefinementPRefinementDimAdaptiveSobol": { "additionalProperties": false, "description": "Estimate dimension preference for automated refinement of stochastic expansion using total Sobol' sensitivity indices.", "properties": { "sobol": { "const": true, "default": true, "description": "Estimate dimension preference for automated refinement of stochastic expansion using total Sobol' sensitivity indices.", "title": "Sobol", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIMENSION_ADAPTIVE_CONTROL_SOBOL" } ] } }, "title": "PceRefinementPRefinementDimAdaptiveSobol", "type": "object" }, "PceRefinementPRefinementUniform": { "additionalProperties": false, "description": "Refine an expansion uniformly in all dimensions.", "properties": { "uniform": { "const": true, "default": true, "description": "Refine an expansion uniformly in all dimensions.", "title": "Uniform", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "UNIFORM_CONTROL" } ] } }, "title": "PceRefinementPRefinementUniform", "type": "object" }, "PceSGLevel": { "additionalProperties": false, "description": "Level to use in sparse grid integration or interpolation", "properties": { "sparse_grid_level": { "$ref": "#/$defs/PceSGLevelConfig", "argument": "level" } }, "required": [ "sparse_grid_level" ], "title": "PceSGLevel", "type": "object" }, "PceSGLevelConfig": { "additionalProperties": false, "description": "Level to use in sparse grid integration or interpolation", "properties": { "level": { "default": 65535, "description": "Level to use in sparse grid integration or interpolation", "title": "Level", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.sparse_grid_level", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "dimension_preference": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "A set of weights specifying the realtive importance of each uncertain variable (dimension)", "title": "Dimension Preference", "x-materialization": [ { "ir_key": "method.nond.dimension_preference", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "growth_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceSGLevelRestricted" }, { "$ref": "#/$defs/PceSGLevelUnrestricted" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Growth", "title": "Growth Rule", "x-union-pattern": 2 }, "nesting_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceSGLevelNested" }, { "$ref": "#/$defs/PceSGLevelNonNested" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Nesting", "title": "Nesting Rule", "x-union-pattern": 2 } }, "title": "PceSGLevelConfig", "type": "object" }, "PceSGLevelNested": { "additionalProperties": false, "description": "Enforce use of nested quadrature rules if available", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "title": "PceSGLevelNested", "type": "object" }, "PceSGLevelNonNested": { "additionalProperties": false, "description": "Enforce use of non-nested quadrature rules", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "title": "PceSGLevelNonNested", "type": "object" }, "PceSGLevelRestricted": { "additionalProperties": false, "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "properties": { "restricted": { "const": true, "default": true, "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "title": "Restricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RESTRICTED" } ] } }, "title": "PceSGLevelRestricted", "type": "object" }, "PceSGLevelUnrestricted": { "additionalProperties": false, "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "properties": { "unrestricted": { "const": true, "default": true, "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "title": "Unrestricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "UNRESTRICTED" } ] } }, "title": "PceSGLevelUnrestricted", "type": "object" }, "Quiet": { "additionalProperties": false, "description": "Level 2 of 5 - less than normal", "properties": { "quiet": { "const": true, "default": true, "description": "Level 2 of 5 - less than normal", "title": "Quiet", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "QUIET_OUTPUT" } ] } }, "title": "Quiet", "type": "object" }, "RefinementMetricCov": { "additionalProperties": false, "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.", "properties": { "covariance": { "const": true, "default": true, "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.", "title": "Covariance", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_metric", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COVARIANCE_METRIC" } ] } }, "title": "RefinementMetricCov", "type": "object" }, "Silent": { "additionalProperties": false, "description": "Level 1 of 5 - minimum", "properties": { "silent": { "const": true, "default": true, "description": "Level 1 of 5 - minimum", "title": "Silent", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SILENT_OUTPUT" } ] } }, "title": "Silent", "type": "object" }, "Verbose": { "additionalProperties": false, "description": "Level 4 of 5 - more than normal", "properties": { "verbose": { "const": true, "default": true, "description": "Level 4 of 5 - more than normal", "title": "Verbose", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VERBOSE_OUTPUT" } ] } }, "title": "Verbose", "type": "object" } }, "additionalProperties": false, "required": [ "coefficient_approach" ] }
- Fields:
import_approx_points_file (dakota.spec.shared.expansion.options.ImportApproxPointsFile | None)probability_levels (dakota.spec.shared.expansion.options.ExpansionOptionsProbabilityLevels | None)reliability_levels (dakota.spec.shared.expansion.options.ExpansionOptionsReliabilityLevels | None)response_levels (dakota.spec.shared.expansion.options.ExpansionOptionsResponseLevels | None)
- field basis_family: PceOptionsAskey | PceOptionsWiener | None = None
Basis Polynomial Family
- field coefficient_approach: PceQuadratureOrder | PceSGLevel | PceCubatureIntegrand | PceExpansionOrder | PceOrthogLeastInterp | PceImportExpansionFile [Required]
Chaos coefficient estimation approach
- field convergence_tolerance: MethodConvergenceTolWithTypeContext2ConvergenceTol | None = None
Stopping criterion based on objective function or statistics convergence
- field covariance_type: ExpansionOptionsDiagCov | ExpansionOptionsFullCov | None = None
Covariance Type
- field distribution: ExpansionOptionsDistributionCumulative | ExpansionOptionsDistributionComplementary [Optional]
Selection of cumulative or complementary cumulative functions
- field export_approx_points_file: ExpansionOptionsExportApproxPointsFile | None = None
Output file for surrogate model value evaluations
- field export_expansion_file: str | None = None
Export the coefficients and multi-index of a Polynomial Chaos Expansion (PCE) to a file
- field final_moments: ExpansionOptionsFinalMomentsNoneKeyword | ExpansionOptionsFinalMomentsStandard | ExpansionOptionsFinalMomentsCentral [Optional]
Output moments of the specified type and include them within the set of final statistics.
- field final_solutions: int = 0
Number of designs returned as the best solutions
- Constraints:
ge = 0
- field fixed_seed: Literal[True] | None = None
Reuses the same seed value for multiple random sampling sets
- field gen_reliability_levels: ExpansionOptionsGenReliabilityLevels | None = None
Specify generalized relability levels at which to estimate the corresponding response value
- field id_method: str | None = None
Name the method block; helpful when there are multiple
- field import_approx_points_file: ImportApproxPointsFile | None = None
Filename for points at which to evaluate the PCE/SC surrogate
- field max_refinement_iterations: int = 9223372036854775807
Maximum number of expansion refinement iterations
- Constraints:
ge = 0
- field model_pointer: str | None = None
Identifier for model block to be used by a method
- field normalized: Literal[True] | None = None
The normalized specification requests output of PCE coefficients that correspond to normalized orthogonal basis polynomials
- field output: Debug | Verbose | Normal | Quiet | Silent [Optional]
Control how much method information is written to the screen and output file
- field p_refinement: PceRefinementPRefinementUniform | PceRefinementPRefinementDimAdaptive | None = None
Automatic polynomial order refinement
- field probability_levels: ExpansionOptionsProbabilityLevels | None = None
Specify probability levels at which to estimate the corresponding response value
- field probability_refinement: ExpansionOptionsProbabilityRefinement | None = None
Allow refinement of probability and generalized reliability results using importance sampling
- field refinement_metric: LevelMappings | RefinementMetricCov | None = None
Metric used for guiding adaptive refinement during UQ.
- field reliability_levels: ExpansionOptionsReliabilityLevels | None = None
Specify reliability levels at which the response values will be estimated
- field response_levels: ExpansionOptionsResponseLevels | None = None
Values at which to estimate desired statistics for each response
- field rng: ExpansionOptionsRngMt19937 | ExpansionOptionsRngRnum2 [Optional]
Selection of a random number generator
- field sample_type: ExpansionOptionsSampleTypeLhs | ExpansionOptionsSampleTypeRandom | None = None
Selection of sampling strategy
- field samples_on_emulator: int = 0
Number of samples at which to evaluate an emulator (surrogate)
- field seed: int | None = None
Seed of the random number generator
- Constraints:
gt = 0
- field variance_based_decomp: ExpansionOptionsVarianceBasedDecomp | None = None
Activates global sensitivity analysis based on decomposition of response variance into main, interaction, and total effects
Generated Pydantic models for method.polynomial_chaos
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocPointsBPDNConfig
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.
Show JSON schema
{ "title": "ExpansionOrderCollocPointsBPDNConfig", "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "type": "object", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field noise_tolerance: list[DakotaFloat] | None = None
The noise tolerance used when performing cross validation in the presence of noise or truncation errors.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocPointsLarsConfig
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
Show JSON schema
{ "title": "ExpansionOrderCollocPointsLarsConfig", "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "type": "object", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field noise_tolerance: list[DakotaFloat] | None = None
The noise tolerance used when performing cross validation in the presence of noise or truncation errors.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioBP
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.
Show JSON schema
{ "title": "ExpansionOrderCollocRatioBP", "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "type": "object", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "additionalProperties": false }
- Fields:
- field basis_pursuit: Literal[True] = True
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioBPDN
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.
Show JSON schema
{ "title": "ExpansionOrderCollocRatioBPDN", "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "type": "object", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/ExpansionOrderCollocRatioBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "$defs": { "ExpansionOrderCollocRatioBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioBPDNConfig", "type": "object" } }, "additionalProperties": false, "required": [ "basis_pursuit_denoising" ] }
- Fields:
- field basis_pursuit_denoising: ExpansionOrderCollocRatioBPDNConfig [Required]
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioBPDNConfig
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.
Show JSON schema
{ "title": "ExpansionOrderCollocRatioBPDNConfig", "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "type": "object", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field noise_tolerance: list[DakotaFloat] | None = None
The noise tolerance used when performing cross validation in the presence of noise or truncation errors.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioCV
Use cross validation to choose the ‘best’ polynomial order of a polynomial chaos expansion.
Show JSON schema
{ "title": "ExpansionOrderCollocRatioCV", "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "type": "object", "properties": { "noise_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Restrict the cross validation process to estimating only the best noise tolerance.", "title": "Noise Only", "x-materialization": [ { "ir_key": "method.nond.cross_validation.noise_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "max_cv_order_candidates": { "default": 65535, "description": "Limit the number of cross-validation candidates for basis order", "minimum": 0, "title": "Max Cv Order Candidates", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.cross_validation.max_order_candidates", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field max_cv_order_candidates: int = 65535
Limit the number of cross-validation candidates for basis order
- Constraints:
ge = 0
- field noise_only: Literal[True] | None = None
Restrict the cross validation process to estimating only the best noise tolerance.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioLars
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
Show JSON schema
{ "title": "ExpansionOrderCollocRatioLars", "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "type": "object", "properties": { "least_angle_regression": { "$ref": "#/$defs/ExpansionOrderCollocRatioLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "$defs": { "ExpansionOrderCollocRatioLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioLarsConfig", "type": "object" } }, "additionalProperties": false, "required": [ "least_angle_regression" ] }
- Fields:
- field least_angle_regression: ExpansionOrderCollocRatioLarsConfig [Required]
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioLarsConfig
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
Show JSON schema
{ "title": "ExpansionOrderCollocRatioLarsConfig", "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "type": "object", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field noise_tolerance: list[DakotaFloat] | None = None
The noise tolerance used when performing cross validation in the presence of noise or truncation errors.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioLassoConfig
Compute the coefficients of a polynomial expansion by using the LASSO problem.
Show JSON schema
{ "title": "ExpansionOrderCollocRatioLassoConfig", "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "type": "object", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field l2_penalty: DakotaFloat | None = None
The \(l_2\) pentalty used when performing compressed sensing with elastic net.
- field noise_tolerance: list[DakotaFloat] | None = None
The noise tolerance used when performing cross validation in the presence of noise or truncation errors.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioLeastSquaresEqCon
Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.
Show JSON schema
{ "title": "ExpansionOrderCollocRatioLeastSquaresEqCon", "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "type": "object", "properties": { "equality_constrained": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "title": "Equality Constrained", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "EQ_CON_LS" } ] } }, "additionalProperties": false }
- field equality_constrained: Literal[True] = True
Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioLeastSquaresSvd
Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.
Show JSON schema
{ "title": "ExpansionOrderCollocRatioLeastSquaresSvd", "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "type": "object", "properties": { "svd": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "title": "Svd", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SVD_LS" } ] } }, "additionalProperties": false }
- Fields:
- field svd: Literal[True] = True
Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.
- pydantic model dakota.spec.method.polynomial_chaos.ExpansionOrderCollocRatioOMPConfig
Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
Show JSON schema
{ "title": "ExpansionOrderCollocRatioOMPConfig", "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "type": "object", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field noise_tolerance: list[DakotaFloat] | None = None
The noise tolerance used when performing cross validation in the presence of noise or truncation errors.
- pydantic model dakota.spec.method.polynomial_chaos.PceCubatureIntegrand
Cubature using Stroud rules and their extensions
Show JSON schema
{ "title": "PceCubatureIntegrand", "description": "Cubature using Stroud rules and their extensions", "type": "object", "properties": { "cubature_integrand": { "description": "Cubature using Stroud rules and their extensions", "title": "Cubature Integrand", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.cubature_integrand", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false, "required": [ "cubature_integrand" ] }
- Fields:
- field cubature_integrand: int [Required]
Cubature using Stroud rules and their extensions
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrder
The (initial) order of a polynomial expansion
Show JSON schema
{ "title": "PceExpansionOrder", "description": "The (initial) order of a polynomial expansion", "type": "object", "properties": { "expansion_order": { "$ref": "#/$defs/PceExpansionOrderConfig", "argument": "order" } }, "$defs": { "ExpansionOrderCollocPointsBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsBPDNConfig", "type": "object" }, "ExpansionOrderCollocPointsLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsLarsConfig", "type": "object" }, "ExpansionOrderCollocRatioBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "ExpansionOrderCollocRatioBP", "type": "object" }, "ExpansionOrderCollocRatioBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/ExpansionOrderCollocRatioBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "required": [ "basis_pursuit_denoising" ], "title": "ExpansionOrderCollocRatioBPDN", "type": "object" }, "ExpansionOrderCollocRatioBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioBPDNConfig", "type": "object" }, "ExpansionOrderCollocRatioCV": { "additionalProperties": false, "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "properties": { "noise_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Restrict the cross validation process to estimating only the best noise tolerance.", "title": "Noise Only", "x-materialization": [ { "ir_key": "method.nond.cross_validation.noise_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "max_cv_order_candidates": { "default": 65535, "description": "Limit the number of cross-validation candidates for basis order", "minimum": 0, "title": "Max Cv Order Candidates", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.cross_validation.max_order_candidates", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioCV", "type": "object" }, "ExpansionOrderCollocRatioLars": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "least_angle_regression": { "$ref": "#/$defs/ExpansionOrderCollocRatioLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "required": [ "least_angle_regression" ], "title": "ExpansionOrderCollocRatioLars", "type": "object" }, "ExpansionOrderCollocRatioLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioLarsConfig", "type": "object" }, "ExpansionOrderCollocRatioLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioLassoConfig", "type": "object" }, "ExpansionOrderCollocRatioLeastSquaresEqCon": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "properties": { "equality_constrained": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "title": "Equality Constrained", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "EQ_CON_LS" } ] } }, "title": "ExpansionOrderCollocRatioLeastSquaresEqCon", "type": "object" }, "ExpansionOrderCollocRatioLeastSquaresSvd": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "properties": { "svd": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "title": "Svd", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SVD_LS" } ] } }, "title": "ExpansionOrderCollocRatioLeastSquaresSvd", "type": "object" }, "ExpansionOrderCollocRatioOMPConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioOMPConfig", "type": "object" }, "PceExpansionOrderBasisTypeAdapted": { "additionalProperties": false, "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "properties": { "adapted": { "$ref": "#/$defs/PceExpansionOrderBasisTypeAdaptedConfig", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ADAPTED_BASIS_EXPANDING_FRONT" } ] } }, "required": [ "adapted" ], "title": "PceExpansionOrderBasisTypeAdapted", "type": "object" }, "PceExpansionOrderBasisTypeAdaptedConfig": { "additionalProperties": false, "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "properties": { "advancements": { "default": 3, "description": "The maximum number of steps used to expand a basis step.", "title": "Advancements", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.adapted_basis.advancements", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "soft_convergence_limit": { "default": 0, "description": "The maximum number of times no improvement in cross validation error is allowed before the algorithm is terminated.", "title": "Soft Convergence Limit", "type": "integer", "x-materialization": [ { "ir_key": "method.soft_convergence_limit", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderBasisTypeAdaptedConfig", "type": "object" }, "PceExpansionOrderBasisTypeTensorProduct": { "additionalProperties": false, "description": "Use a tensor-product index set to construct a polynomial chaos expansion.", "properties": { "tensor_product": { "const": true, "default": true, "description": "Use a tensor-product index set to construct a polynomial chaos expansion.", "title": "Tensor Product", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "TENSOR_PRODUCT_BASIS" } ] } }, "title": "PceExpansionOrderBasisTypeTensorProduct", "type": "object" }, "PceExpansionOrderBasisTypeTotalOrder": { "additionalProperties": false, "description": "Use a total-order index set to construct a polynomial chaos expansion.", "properties": { "total_order": { "const": true, "default": true, "description": "Use a total-order index set to construct a polynomial chaos expansion.", "title": "Total Order", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": 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"default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "PceExpansionOrderImportBuildPointsFile", "type": "object" }, "PceExpansionOrderImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileAnnotated", "type": "object" }, "PceExpansionOrderImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotated", "type": "object" }, "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceExpansionOrderImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "expansion_order" ] }
- field expansion_order: PceExpansionOrderConfig [Required]
The (initial) order of a polynomial expansion
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderBasisTypeAdapted
Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.
Show JSON schema
{ "title": "PceExpansionOrderBasisTypeAdapted", "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "type": "object", "properties": { "adapted": { "$ref": "#/$defs/PceExpansionOrderBasisTypeAdaptedConfig", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ADAPTED_BASIS_EXPANDING_FRONT" } ] } }, "$defs": { "PceExpansionOrderBasisTypeAdaptedConfig": { "additionalProperties": false, "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "properties": { "advancements": { "default": 3, "description": "The maximum number of steps used to expand a basis step.", "title": "Advancements", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.adapted_basis.advancements", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "soft_convergence_limit": { "default": 0, "description": "The maximum number of times no improvement in cross validation error is allowed before the algorithm is terminated.", "title": "Soft Convergence Limit", "type": "integer", "x-materialization": [ { "ir_key": "method.soft_convergence_limit", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderBasisTypeAdaptedConfig", "type": "object" } }, "additionalProperties": false, "required": [ "adapted" ] }
- field adapted: PceExpansionOrderBasisTypeAdaptedConfig [Required]
Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderBasisTypeAdaptedConfig
Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.
Show JSON schema
{ "title": "PceExpansionOrderBasisTypeAdaptedConfig", "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "type": "object", "properties": { "advancements": { "default": 3, "description": "The maximum number of steps used to expand a basis step.", "title": "Advancements", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.adapted_basis.advancements", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "soft_convergence_limit": { "default": 0, "description": "The maximum number of times no improvement in cross validation error is allowed before the algorithm is terminated.", "title": "Soft Convergence Limit", "type": "integer", "x-materialization": [ { "ir_key": "method.soft_convergence_limit", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field advancements: int = 3
The maximum number of steps used to expand a basis step.
- field soft_convergence_limit: int = 0
The maximum number of times no improvement in cross validation error is allowed before the algorithm is terminated.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderBasisTypeTensorProduct
Use a tensor-product index set to construct a polynomial chaos expansion.
Show JSON schema
{ "title": "PceExpansionOrderBasisTypeTensorProduct", "description": "Use a tensor-product index set to construct a polynomial chaos expansion.", "type": "object", "properties": { "tensor_product": { "const": true, "default": true, "description": "Use a tensor-product index set to construct a polynomial chaos expansion.", "title": "Tensor Product", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "TENSOR_PRODUCT_BASIS" } ] } }, "additionalProperties": false }
- Fields:
- field tensor_product: Literal[True] = True
Use a tensor-product index set to construct a polynomial chaos expansion.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderBasisTypeTotalOrder
Use a total-order index set to construct a polynomial chaos expansion.
Show JSON schema
{ "title": "PceExpansionOrderBasisTypeTotalOrder", "description": "Use a total-order index set to construct a polynomial chaos expansion.", "type": "object", "properties": { "total_order": { "const": true, "default": true, "description": "Use a total-order index set to construct a polynomial chaos expansion.", "title": "Total Order", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "TOTAL_ORDER_BASIS" } ] } }, "additionalProperties": false }
- Fields:
- field total_order: Literal[True] = True
Use a total-order index set to construct a polynomial chaos expansion.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPoints
Number of collocation points used to estimate expansion coefficients
Show JSON schema
{ "title": "PceExpansionOrderCollocPoints", "description": "Number of collocation points used to estimate expansion coefficients", "type": "object", "properties": { "collocation_points": { "$ref": "#/$defs/PceExpansionOrderCollocPointsConfig", "argument": "points" } }, "$defs": { "ExpansionOrderCollocPointsBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsBPDNConfig", "type": "object" }, "ExpansionOrderCollocPointsLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsLarsConfig", "type": "object" }, "PceExpansionOrderCollocPointsBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "PceExpansionOrderCollocPointsBP", "type": "object" }, "PceExpansionOrderCollocPointsBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/ExpansionOrderCollocPointsBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "required": [ "basis_pursuit_denoising" ], "title": "PceExpansionOrderCollocPointsBPDN", "type": "object" }, "PceExpansionOrderCollocPointsCV": { "additionalProperties": false, "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "properties": { "noise_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Restrict the cross validation process to estimating only the best noise tolerance.", "title": "Noise Only", "x-materialization": [ { "ir_key": "method.nond.cross_validation.noise_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "max_cv_order_candidates": { "default": 65535, "description": "Limit the number of cross-validation candidates for basis order", "minimum": 0, "title": "Max Cv Order Candidates", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.cross_validation.max_order_candidates", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsCV", "type": "object" }, "PceExpansionOrderCollocPointsConfig": { "additionalProperties": false, "description": "Number of collocation points used to estimate expansion coefficients", "properties": { "points": { "description": "Number of collocation points used to estimate expansion coefficients", "title": "Points", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.collocation_points", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "regression_method": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquares" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsOMP" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsBP" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsBPDN" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLars" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLasso" }, { "type": "null" } ], "default": null, "description": "Regression Algorithm", "title": "Regression Method", "x-union-pattern": 2 }, "cross_validation": { "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsCV" }, { "type": "null" } ], "default": null, "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "x-materialization": [ { "ir_key": "method.nond.cross_validation", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "ratio_order": { "default": 1.0, "description": "Specify a non-linear the relationship between the expansion order of a polynomial chaos expansion and the number of samples that will be used to compute the PCE coefficients.", "exclusiveMinimum": 0, "title": "Ratio Order", "type": "number", "x-materialization": [ { "ir_key": "method.nond.collocation_ratio_terms_order", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "response_scaling": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Perform bounds-scaling on response values prior to surrogate emulation", "title": "Response Scaling", "x-materialization": [ { "ir_key": "method.nond.response_scaling", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "use_derivatives": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Use derivative data to construct surrogate models", "title": "Use Derivatives", "x-materialization": [ { "ir_key": "method.derivative_usage", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "tensor_grid": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.", "title": "Tensor Grid", "x-materialization": [ { "ir_key": "method.nond.tensor_grid", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] }, "max_solver_iterations": { "default": 9223372036854775807, "description": "Maximum iterations in determining polynomial coefficients", "minimum": 0, "title": "Max Solver Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.max_solver_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "points" ], "title": "PceExpansionOrderCollocPointsConfig", "type": "object" }, "PceExpansionOrderCollocPointsLars": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "least_angle_regression": { "$ref": "#/$defs/ExpansionOrderCollocPointsLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "required": [ "least_angle_regression" ], "title": "PceExpansionOrderCollocPointsLars", "type": "object" }, "PceExpansionOrderCollocPointsLasso": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/PceExpansionOrderCollocPointsLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "required": [ "least_absolute_shrinkage" ], "title": "PceExpansionOrderCollocPointsLasso", "type": "object" }, "PceExpansionOrderCollocPointsLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsLassoConfig", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquares": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using least squares", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquaresSvd" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquaresEqCon" }, { "additionalProperties": true, "type": "object" } ], "description": "Compute the coefficients of a polynomial expansion using least squares", "title": "Least Squares", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEFAULT_LEAST_SQ_REGRESSION" } ] } }, "required": [ "least_squares" ], "title": "PceExpansionOrderCollocPointsLeastSquares", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquaresEqCon": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "properties": { "equality_constrained": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "title": "Equality Constrained", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "EQ_CON_LS" } ] } }, "title": "PceExpansionOrderCollocPointsLeastSquaresEqCon", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquaresSvd": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "properties": { "svd": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "title": "Svd", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SVD_LS" } ] } }, "title": "PceExpansionOrderCollocPointsLeastSquaresSvd", "type": "object" }, "PceExpansionOrderCollocPointsOMP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/PceExpansionOrderCollocPointsOMPConfig", "x-aliases": [ "omp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_MATCH_PURSUIT" } ] } }, "required": [ "orthogonal_matching_pursuit" ], "title": "PceExpansionOrderCollocPointsOMP", "type": "object" }, "PceExpansionOrderCollocPointsOMPConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsOMPConfig", "type": "object" } }, "additionalProperties": false, "required": [ "collocation_points" ] }
- Fields:
- field collocation_points: PceExpansionOrderCollocPointsConfig [Required]
Number of collocation points used to estimate expansion coefficients
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsBP
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsBP", "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "type": "object", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "additionalProperties": false }
- Fields:
- field basis_pursuit: Literal[True] = True
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsBPDN
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsBPDN", "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "type": "object", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/ExpansionOrderCollocPointsBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "$defs": { "ExpansionOrderCollocPointsBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsBPDNConfig", "type": "object" } }, "additionalProperties": false, "required": [ "basis_pursuit_denoising" ] }
- Fields:
- field basis_pursuit_denoising: ExpansionOrderCollocPointsBPDNConfig [Required]
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsCV
Use cross validation to choose the ‘best’ polynomial order of a polynomial chaos expansion.
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsCV", "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "type": "object", "properties": { "noise_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Restrict the cross validation process to estimating only the best noise tolerance.", "title": "Noise Only", "x-materialization": [ { "ir_key": "method.nond.cross_validation.noise_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "max_cv_order_candidates": { "default": 65535, "description": "Limit the number of cross-validation candidates for basis order", "minimum": 0, "title": "Max Cv Order Candidates", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.cross_validation.max_order_candidates", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field max_cv_order_candidates: int = 65535
Limit the number of cross-validation candidates for basis order
- Constraints:
ge = 0
- field noise_only: Literal[True] | None = None
Restrict the cross validation process to estimating only the best noise tolerance.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsConfig
Number of collocation points used to estimate expansion coefficients
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsConfig", "description": "Number of collocation points used to estimate expansion coefficients", "type": "object", "properties": { "points": { "description": "Number of collocation points used to estimate expansion coefficients", "title": "Points", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.collocation_points", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "regression_method": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquares" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsOMP" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsBP" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsBPDN" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLars" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLasso" }, { "type": "null" } ], "default": null, "description": "Regression Algorithm", "title": "Regression Method", "x-union-pattern": 2 }, "cross_validation": { "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsCV" }, { "type": "null" } ], "default": null, "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "x-materialization": [ { "ir_key": "method.nond.cross_validation", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "ratio_order": { "default": 1.0, "description": "Specify a non-linear the relationship between the expansion order of a polynomial chaos expansion and the number of samples that will be used to compute the PCE coefficients.", "exclusiveMinimum": 0, "title": "Ratio Order", "type": "number", "x-materialization": [ { "ir_key": "method.nond.collocation_ratio_terms_order", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "response_scaling": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Perform bounds-scaling on response values prior to surrogate emulation", "title": "Response Scaling", "x-materialization": [ { "ir_key": "method.nond.response_scaling", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "use_derivatives": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Use derivative data to construct surrogate models", "title": "Use Derivatives", "x-materialization": [ { "ir_key": "method.derivative_usage", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "tensor_grid": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.", "title": "Tensor Grid", "x-materialization": [ { "ir_key": "method.nond.tensor_grid", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] }, "max_solver_iterations": { "default": 9223372036854775807, "description": "Maximum iterations in determining polynomial coefficients", "minimum": 0, "title": "Max Solver Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.max_solver_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] } }, "$defs": { "ExpansionOrderCollocPointsBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsBPDNConfig", "type": "object" }, "ExpansionOrderCollocPointsLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsLarsConfig", "type": "object" }, "PceExpansionOrderCollocPointsBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "PceExpansionOrderCollocPointsBP", "type": "object" }, "PceExpansionOrderCollocPointsBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/ExpansionOrderCollocPointsBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "required": [ "basis_pursuit_denoising" ], "title": "PceExpansionOrderCollocPointsBPDN", "type": "object" }, "PceExpansionOrderCollocPointsCV": { "additionalProperties": false, "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "properties": { "noise_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Restrict the cross validation process to estimating only the best noise tolerance.", "title": "Noise Only", "x-materialization": [ { "ir_key": "method.nond.cross_validation.noise_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "max_cv_order_candidates": { "default": 65535, "description": "Limit the number of cross-validation candidates for basis order", "minimum": 0, "title": "Max Cv Order Candidates", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.cross_validation.max_order_candidates", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsCV", "type": "object" }, "PceExpansionOrderCollocPointsLars": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "least_angle_regression": { "$ref": "#/$defs/ExpansionOrderCollocPointsLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "required": [ "least_angle_regression" ], "title": "PceExpansionOrderCollocPointsLars", "type": "object" }, "PceExpansionOrderCollocPointsLasso": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/PceExpansionOrderCollocPointsLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "required": [ "least_absolute_shrinkage" ], "title": "PceExpansionOrderCollocPointsLasso", "type": "object" }, "PceExpansionOrderCollocPointsLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsLassoConfig", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquares": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using least squares", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquaresSvd" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquaresEqCon" }, { "additionalProperties": true, "type": "object" } ], "description": "Compute the coefficients of a polynomial expansion using least squares", "title": "Least Squares", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEFAULT_LEAST_SQ_REGRESSION" } ] } }, "required": [ "least_squares" ], "title": "PceExpansionOrderCollocPointsLeastSquares", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquaresEqCon": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "properties": { "equality_constrained": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "title": "Equality Constrained", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "EQ_CON_LS" } ] } }, "title": "PceExpansionOrderCollocPointsLeastSquaresEqCon", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquaresSvd": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "properties": { "svd": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "title": "Svd", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SVD_LS" } ] } }, "title": "PceExpansionOrderCollocPointsLeastSquaresSvd", "type": "object" }, "PceExpansionOrderCollocPointsOMP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/PceExpansionOrderCollocPointsOMPConfig", "x-aliases": [ "omp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_MATCH_PURSUIT" } ] } }, "required": [ "orthogonal_matching_pursuit" ], "title": "PceExpansionOrderCollocPointsOMP", "type": "object" }, "PceExpansionOrderCollocPointsOMPConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsOMPConfig", "type": "object" } }, "additionalProperties": false, "required": [ "points" ] }
- Fields:
- field cross_validation: PceExpansionOrderCollocPointsCV | None = None
Use cross validation to choose the ‘best’ polynomial order of a polynomial chaos expansion.
- field max_solver_iterations: int = 9223372036854775807
Maximum iterations in determining polynomial coefficients
- Constraints:
ge = 0
- field points: int [Required]
Number of collocation points used to estimate expansion coefficients
- field ratio_order: DakotaFloat = 1.0
Specify a non-linear the relationship between the expansion order of a polynomial chaos expansion and the number of samples that will be used to compute the PCE coefficients.
- Constraints:
gt = 0
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- field regression_method: PceExpansionOrderCollocPointsLeastSquares | PceExpansionOrderCollocPointsOMP | PceExpansionOrderCollocPointsBP | PceExpansionOrderCollocPointsBPDN | PceExpansionOrderCollocPointsLars | PceExpansionOrderCollocPointsLasso | None = None
Regression Algorithm
- field response_scaling: Literal[True] | None = None
Perform bounds-scaling on response values prior to surrogate emulation
- field reuse_points: Literal[True] | None = None
This describes the behavior of reuse of points in constructing polynomial chaos expansion models.
- field tensor_grid: Literal[True] | None = None
Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.
- field use_derivatives: Literal[True] | None = None
Use derivative data to construct surrogate models
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsLars
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsLars", "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "type": "object", "properties": { "least_angle_regression": { "$ref": "#/$defs/ExpansionOrderCollocPointsLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "$defs": { "ExpansionOrderCollocPointsLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsLarsConfig", "type": "object" } }, "additionalProperties": false, "required": [ "least_angle_regression" ] }
- Fields:
- field least_angle_regression: ExpansionOrderCollocPointsLarsConfig [Required]
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsLasso
Compute the coefficients of a polynomial expansion by using the LASSO problem.
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsLasso", "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "type": "object", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/PceExpansionOrderCollocPointsLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "$defs": { "PceExpansionOrderCollocPointsLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsLassoConfig", "type": "object" } }, "additionalProperties": false, "required": [ "least_absolute_shrinkage" ] }
- field least_absolute_shrinkage: PceExpansionOrderCollocPointsLassoConfig [Required]
Compute the coefficients of a polynomial expansion by using the LASSO problem.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsLassoConfig
Compute the coefficients of a polynomial expansion by using the LASSO problem.
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsLassoConfig", "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "type": "object", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field l2_penalty: DakotaFloat | None = None
The \(l_2\) pentalty used when performing compressed sensing with elastic net.
- field noise_tolerance: list[DakotaFloat] | None = None
The noise tolerance used when performing cross validation in the presence of noise or truncation errors.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsLeastSquares
Compute the coefficients of a polynomial expansion using least squares
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsLeastSquares", "description": "Compute the coefficients of a polynomial expansion using least squares", "type": "object", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquaresSvd" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquaresEqCon" }, { "additionalProperties": true, "type": "object" } ], "description": "Compute the coefficients of a polynomial expansion using least squares", "title": "Least Squares", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEFAULT_LEAST_SQ_REGRESSION" } ] } }, "$defs": { "PceExpansionOrderCollocPointsLeastSquaresEqCon": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "properties": { "equality_constrained": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "title": "Equality Constrained", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "EQ_CON_LS" } ] } }, "title": "PceExpansionOrderCollocPointsLeastSquaresEqCon", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquaresSvd": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "properties": { "svd": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "title": "Svd", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SVD_LS" } ] } }, "title": "PceExpansionOrderCollocPointsLeastSquaresSvd", "type": "object" } }, "additionalProperties": false, "required": [ "least_squares" ] }
- field least_squares: PceExpansionOrderCollocPointsLeastSquaresSvd | PceExpansionOrderCollocPointsLeastSquaresEqCon | dict [Required]
Compute the coefficients of a polynomial expansion using least squares
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsLeastSquaresEqCon
Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsLeastSquaresEqCon", "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "type": "object", "properties": { "equality_constrained": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "title": "Equality Constrained", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "EQ_CON_LS" } ] } }, "additionalProperties": false }
- field equality_constrained: Literal[True] = True
Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsLeastSquaresSvd
Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsLeastSquaresSvd", "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "type": "object", "properties": { "svd": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "title": "Svd", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SVD_LS" } ] } }, "additionalProperties": false }
- Fields:
- field svd: Literal[True] = True
Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsOMP
Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsOMP", "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "type": "object", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/PceExpansionOrderCollocPointsOMPConfig", "x-aliases": [ "omp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_MATCH_PURSUIT" } ] } }, "$defs": { "PceExpansionOrderCollocPointsOMPConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsOMPConfig", "type": "object" } }, "additionalProperties": false, "required": [ "orthogonal_matching_pursuit" ] }
- field orthogonal_matching_pursuit: PceExpansionOrderCollocPointsOMPConfig [Required]
Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocPointsOMPConfig
Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
Show JSON schema
{ "title": "PceExpansionOrderCollocPointsOMPConfig", "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "type": "object", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- field noise_tolerance: list[DakotaFloat] | None = None
The noise tolerance used when performing cross validation in the presence of noise or truncation errors.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocRatio
Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.
Show JSON schema
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- field collocation_ratio: PceExpansionOrderCollocRatioConfig [Required]
Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocRatioConfig
Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.
Show JSON schema
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the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioOMPConfig", "type": "object" }, "PceExpansionOrderCollocRatioLasso": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/ExpansionOrderCollocRatioLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "required": [ "least_absolute_shrinkage" ], "title": "PceExpansionOrderCollocRatioLasso", "type": "object" }, "PceExpansionOrderCollocRatioLeastSquares": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using least squares", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/ExpansionOrderCollocRatioLeastSquaresSvd" }, { "$ref": "#/$defs/ExpansionOrderCollocRatioLeastSquaresEqCon" }, { "additionalProperties": true, "type": "object" } ], "description": "Compute the coefficients of a polynomial expansion using least squares", "title": "Least Squares", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEFAULT_LEAST_SQ_REGRESSION" } ] } }, "required": [ "least_squares" ], "title": "PceExpansionOrderCollocRatioLeastSquares", "type": "object" }, "PceExpansionOrderCollocRatioOMP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/ExpansionOrderCollocRatioOMPConfig", "x-aliases": [ "omp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_MATCH_PURSUIT" } ] } }, "required": [ "orthogonal_matching_pursuit" ], "title": "PceExpansionOrderCollocRatioOMP", "type": "object" } }, "additionalProperties": false, "required": [ "value" ] }
- field cross_validation: ExpansionOrderCollocRatioCV | None = None
Use cross validation to choose the ‘best’ polynomial order of a polynomial chaos expansion.
- field max_solver_iterations: int = 9223372036854775807
Maximum iterations in determining polynomial coefficients
- Constraints:
ge = 0
- field ratio_order: DakotaFloat = 1.0
Specify a non-linear the relationship between the expansion order of a polynomial chaos expansion and the number of samples that will be used to compute the PCE coefficients.
- Constraints:
gt = 0
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- field regression_method: PceExpansionOrderCollocRatioLeastSquares | PceExpansionOrderCollocRatioOMP | ExpansionOrderCollocRatioBP | ExpansionOrderCollocRatioBPDN | ExpansionOrderCollocRatioLars | PceExpansionOrderCollocRatioLasso | None = None
Regression Algorithm
- field response_scaling: Literal[True] | None = None
Perform bounds-scaling on response values prior to surrogate emulation
- field reuse_points: Literal[True] | None = None
This describes the behavior of reuse of points in constructing polynomial chaos expansion models.
- field tensor_grid: Literal[True] | None = None
Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.
- field use_derivatives: Literal[True] | None = None
Use derivative data to construct surrogate models
- field value: DakotaFloat [Required]
Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.
- Constraints:
gt = 0
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocRatioLasso
Compute the coefficients of a polynomial expansion by using the LASSO problem.
Show JSON schema
{ "title": "PceExpansionOrderCollocRatioLasso", "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "type": "object", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/ExpansionOrderCollocRatioLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "$defs": { "ExpansionOrderCollocRatioLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioLassoConfig", "type": "object" } }, "additionalProperties": false, "required": [ "least_absolute_shrinkage" ] }
- Fields:
- field least_absolute_shrinkage: ExpansionOrderCollocRatioLassoConfig [Required]
Compute the coefficients of a polynomial expansion by using the LASSO problem.
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocRatioLeastSquares
Compute the coefficients of a polynomial expansion using least squares
Show JSON schema
{ "title": "PceExpansionOrderCollocRatioLeastSquares", "description": "Compute the coefficients of a polynomial expansion using least squares", "type": "object", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/ExpansionOrderCollocRatioLeastSquaresSvd" }, { "$ref": "#/$defs/ExpansionOrderCollocRatioLeastSquaresEqCon" }, { "additionalProperties": true, "type": "object" } ], "description": "Compute the coefficients of a polynomial expansion using least squares", "title": "Least Squares", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEFAULT_LEAST_SQ_REGRESSION" } ] } }, "$defs": { "ExpansionOrderCollocRatioLeastSquaresEqCon": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "properties": { "equality_constrained": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "title": "Equality Constrained", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "EQ_CON_LS" } ] } }, "title": "ExpansionOrderCollocRatioLeastSquaresEqCon", "type": "object" }, "ExpansionOrderCollocRatioLeastSquaresSvd": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "properties": { "svd": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "title": "Svd", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SVD_LS" } ] } }, "title": "ExpansionOrderCollocRatioLeastSquaresSvd", "type": "object" } }, "additionalProperties": false, "required": [ "least_squares" ] }
- field least_squares: ExpansionOrderCollocRatioLeastSquaresSvd | ExpansionOrderCollocRatioLeastSquaresEqCon | dict [Required]
Compute the coefficients of a polynomial expansion using least squares
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderCollocRatioOMP
Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
Show JSON schema
{ "title": "PceExpansionOrderCollocRatioOMP", "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "type": "object", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/ExpansionOrderCollocRatioOMPConfig", "x-aliases": [ "omp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_MATCH_PURSUIT" } ] } }, "$defs": { "ExpansionOrderCollocRatioOMPConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioOMPConfig", "type": "object" } }, "additionalProperties": false, "required": [ "orthogonal_matching_pursuit" ] }
- Fields:
- field orthogonal_matching_pursuit: ExpansionOrderCollocRatioOMPConfig [Required]
Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderConfig
The (initial) order of a polynomial expansion
Show JSON schema
{ "title": "PceExpansionOrderConfig", "description": "The (initial) order of a polynomial expansion", "type": "object", "properties": { "order": { "default": 65535, "description": "The (initial) order of a polynomial expansion", "title": "Order", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.expansion_order", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "dimension_preference": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "A set of weights specifying the realtive importance of each uncertain variable (dimension)", "title": "Dimension Preference", "x-materialization": [ { "ir_key": "method.nond.dimension_preference", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "basis_type": { "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderBasisTypeTensorProduct" }, { "$ref": "#/$defs/PceExpansionOrderBasisTypeTotalOrder" }, { "$ref": "#/$defs/PceExpansionOrderBasisTypeAdapted" }, { "type": "null" } ], "default": null, "description": "Specify the type of basis truncation to be used for a Polynomial Chaos Expansion.", "title": "Basis Type", "x-union-pattern": 2 }, "point_selection": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPoints" }, { "$ref": "#/$defs/PceExpansionOrderCollocRatio" }, { "$ref": "#/$defs/PceExpansionOrderExpansionSamples" } ], "title": "Point Selection", "x-union-pattern": 4 }, "import_build_points_file": { "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] } }, "$defs": { "ExpansionOrderCollocPointsBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsBPDNConfig", "type": "object" }, "ExpansionOrderCollocPointsLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocPointsLarsConfig", "type": "object" }, "ExpansionOrderCollocRatioBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "ExpansionOrderCollocRatioBP", "type": "object" }, "ExpansionOrderCollocRatioBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/ExpansionOrderCollocRatioBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "required": [ "basis_pursuit_denoising" ], "title": "ExpansionOrderCollocRatioBPDN", "type": "object" }, "ExpansionOrderCollocRatioBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioBPDNConfig", "type": "object" }, "ExpansionOrderCollocRatioCV": { "additionalProperties": false, "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "properties": { "noise_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Restrict the cross validation process to estimating only the best noise tolerance.", "title": "Noise Only", "x-materialization": [ { "ir_key": "method.nond.cross_validation.noise_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "max_cv_order_candidates": { "default": 65535, "description": "Limit the number of cross-validation candidates for basis order", "minimum": 0, "title": "Max Cv Order Candidates", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.cross_validation.max_order_candidates", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioCV", "type": "object" }, "ExpansionOrderCollocRatioLars": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "least_angle_regression": { "$ref": "#/$defs/ExpansionOrderCollocRatioLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "required": [ "least_angle_regression" ], "title": "ExpansionOrderCollocRatioLars", "type": "object" }, "ExpansionOrderCollocRatioLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioLarsConfig", "type": "object" }, "ExpansionOrderCollocRatioLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioLassoConfig", "type": "object" }, "ExpansionOrderCollocRatioLeastSquaresEqCon": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "properties": { "equality_constrained": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "title": "Equality Constrained", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "EQ_CON_LS" } ] } }, "title": "ExpansionOrderCollocRatioLeastSquaresEqCon", "type": "object" }, "ExpansionOrderCollocRatioLeastSquaresSvd": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "properties": { "svd": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "title": "Svd", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SVD_LS" } ] } }, "title": "ExpansionOrderCollocRatioLeastSquaresSvd", "type": "object" }, "ExpansionOrderCollocRatioOMPConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOrderCollocRatioOMPConfig", "type": "object" }, "PceExpansionOrderBasisTypeAdapted": { "additionalProperties": false, "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "properties": { "adapted": { "$ref": "#/$defs/PceExpansionOrderBasisTypeAdaptedConfig", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ADAPTED_BASIS_EXPANDING_FRONT" } ] } }, "required": [ "adapted" ], "title": "PceExpansionOrderBasisTypeAdapted", "type": "object" }, "PceExpansionOrderBasisTypeAdaptedConfig": { "additionalProperties": false, "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "properties": { "advancements": { "default": 3, "description": "The maximum number of steps used to expand a basis step.", "title": "Advancements", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.adapted_basis.advancements", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "soft_convergence_limit": { "default": 0, "description": "The maximum number of times no improvement in cross validation error is allowed before the algorithm is terminated.", "title": "Soft Convergence Limit", "type": "integer", "x-materialization": [ { "ir_key": "method.soft_convergence_limit", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderBasisTypeAdaptedConfig", "type": "object" }, "PceExpansionOrderBasisTypeTensorProduct": { "additionalProperties": false, "description": "Use a tensor-product index set to construct a polynomial chaos expansion.", "properties": { "tensor_product": { "const": true, "default": true, "description": "Use a tensor-product index set to construct a polynomial chaos expansion.", "title": "Tensor Product", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "TENSOR_PRODUCT_BASIS" } ] } }, "title": "PceExpansionOrderBasisTypeTensorProduct", "type": "object" }, "PceExpansionOrderBasisTypeTotalOrder": { "additionalProperties": false, "description": "Use a total-order index set to construct a polynomial chaos expansion.", "properties": { "total_order": { "const": true, "default": true, "description": "Use a total-order index set to construct a polynomial chaos expansion.", "title": "Total Order", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "TOTAL_ORDER_BASIS" } ] } }, "title": "PceExpansionOrderBasisTypeTotalOrder", "type": "object" }, "PceExpansionOrderCollocPoints": { "additionalProperties": false, "description": "Number of collocation points used to estimate expansion coefficients", "properties": { "collocation_points": { "$ref": "#/$defs/PceExpansionOrderCollocPointsConfig", "argument": "points" } }, "required": [ "collocation_points" ], "title": "PceExpansionOrderCollocPoints", "type": "object" }, "PceExpansionOrderCollocPointsBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "PceExpansionOrderCollocPointsBP", "type": "object" }, "PceExpansionOrderCollocPointsBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/ExpansionOrderCollocPointsBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "required": [ "basis_pursuit_denoising" ], "title": "PceExpansionOrderCollocPointsBPDN", "type": "object" }, "PceExpansionOrderCollocPointsCV": { "additionalProperties": false, "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "properties": { "noise_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Restrict the cross validation process to estimating only the best noise tolerance.", "title": "Noise Only", "x-materialization": [ { "ir_key": "method.nond.cross_validation.noise_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "max_cv_order_candidates": { "default": 65535, "description": "Limit the number of cross-validation candidates for basis order", "minimum": 0, "title": "Max Cv Order Candidates", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.cross_validation.max_order_candidates", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsCV", "type": "object" }, "PceExpansionOrderCollocPointsConfig": { "additionalProperties": false, "description": "Number of collocation points used to estimate expansion coefficients", "properties": { "points": { "description": "Number of collocation points used to estimate expansion coefficients", "title": "Points", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.collocation_points", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "regression_method": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquares" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsOMP" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsBP" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsBPDN" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLars" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLasso" }, { "type": "null" } ], "default": null, "description": "Regression Algorithm", "title": "Regression Method", "x-union-pattern": 2 }, "cross_validation": { "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsCV" }, { "type": "null" } ], "default": null, "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "x-materialization": [ { "ir_key": "method.nond.cross_validation", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "ratio_order": { "default": 1.0, "description": "Specify a non-linear the relationship between the expansion order of a polynomial chaos expansion and the number of samples that will be used to compute the PCE coefficients.", "exclusiveMinimum": 0, "title": "Ratio Order", "type": "number", "x-materialization": [ { "ir_key": "method.nond.collocation_ratio_terms_order", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "response_scaling": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Perform bounds-scaling on response values prior to surrogate emulation", "title": "Response Scaling", "x-materialization": [ { "ir_key": "method.nond.response_scaling", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "use_derivatives": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Use derivative data to construct surrogate models", "title": "Use Derivatives", "x-materialization": [ { "ir_key": "method.derivative_usage", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "tensor_grid": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": 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"storage_type": "DIRECT_VALUE" } ] } }, "required": [ "points" ], "title": "PceExpansionOrderCollocPointsConfig", "type": "object" }, "PceExpansionOrderCollocPointsLars": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "least_angle_regression": { "$ref": "#/$defs/ExpansionOrderCollocPointsLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "required": [ "least_angle_regression" ], "title": "PceExpansionOrderCollocPointsLars", "type": "object" }, "PceExpansionOrderCollocPointsLasso": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/PceExpansionOrderCollocPointsLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "required": [ "least_absolute_shrinkage" ], "title": "PceExpansionOrderCollocPointsLasso", "type": "object" }, "PceExpansionOrderCollocPointsLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsLassoConfig", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquares": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using least squares", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquaresSvd" }, { "$ref": "#/$defs/PceExpansionOrderCollocPointsLeastSquaresEqCon" }, { "additionalProperties": true, "type": "object" } ], "description": "Compute the coefficients of a polynomial expansion using least squares", "title": "Least Squares", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEFAULT_LEAST_SQ_REGRESSION" } ] } }, "required": [ "least_squares" ], "title": "PceExpansionOrderCollocPointsLeastSquares", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquaresEqCon": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "properties": { "equality_constrained": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.", "title": "Equality Constrained", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "EQ_CON_LS" } ] } }, "title": "PceExpansionOrderCollocPointsLeastSquaresEqCon", "type": "object" }, "PceExpansionOrderCollocPointsLeastSquaresSvd": { "additionalProperties": false, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "properties": { "svd": { "const": true, "default": true, "description": "Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.", "title": "Svd", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.least_squares_regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SVD_LS" } ] } }, "title": "PceExpansionOrderCollocPointsLeastSquaresSvd", "type": "object" }, "PceExpansionOrderCollocPointsOMP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/PceExpansionOrderCollocPointsOMPConfig", "x-aliases": [ "omp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_MATCH_PURSUIT" } ] } }, "required": [ "orthogonal_matching_pursuit" ], "title": "PceExpansionOrderCollocPointsOMP", "type": "object" }, "PceExpansionOrderCollocPointsOMPConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "PceExpansionOrderCollocPointsOMPConfig", "type": "object" }, "PceExpansionOrderCollocRatio": { "additionalProperties": false, "description": "Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.", "properties": { "collocation_ratio": { "$ref": "#/$defs/PceExpansionOrderCollocRatioConfig", "argument": "value" } }, "required": [ "collocation_ratio" ], "title": "PceExpansionOrderCollocRatio", "type": "object" }, "PceExpansionOrderCollocRatioConfig": { "additionalProperties": false, "description": "Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.", "properties": { "value": { "description": "Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.", "exclusiveMinimum": 0, "title": "Value", "type": "number", "x-materialization": [ { "ir_key": "method.nond.collocation_ratio", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "regression_method": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderCollocRatioLeastSquares" }, { "$ref": "#/$defs/PceExpansionOrderCollocRatioOMP" }, { "$ref": "#/$defs/ExpansionOrderCollocRatioBP" }, { "$ref": "#/$defs/ExpansionOrderCollocRatioBPDN" }, { "$ref": "#/$defs/ExpansionOrderCollocRatioLars" }, { "$ref": "#/$defs/PceExpansionOrderCollocRatioLasso" }, { "type": "null" } ], "default": null, "description": "Regression Algorithm", "title": "Regression Method", "x-union-pattern": 2 }, "cross_validation": { "anyOf": [ { "$ref": "#/$defs/ExpansionOrderCollocRatioCV" }, { "type": "null" } ], "default": null, "description": "Use cross validation to choose the 'best' polynomial order of a polynomial chaos expansion.", "x-materialization": [ { "ir_key": "method.nond.cross_validation", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "ratio_order": { "default": 1.0, "description": "Specify a non-linear the relationship between the expansion order of a polynomial chaos expansion and the number of samples that will be used to compute the PCE coefficients.", "exclusiveMinimum": 0, "title": "Ratio Order", "type": "number", "x-materialization": [ { "ir_key": "method.nond.collocation_ratio_terms_order", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "response_scaling": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Perform bounds-scaling on response values prior to surrogate emulation", "title": "Response Scaling", "x-materialization": [ { "ir_key": "method.nond.response_scaling", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "use_derivatives": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Use derivative data to construct surrogate models", "title": "Use Derivatives", "x-materialization": [ { "ir_key": "method.derivative_usage", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "tensor_grid": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.", "title": "Tensor Grid", "x-materialization": [ { "ir_key": "method.nond.tensor_grid", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] }, "max_solver_iterations": { "default": 9223372036854775807, "description": "Maximum iterations in determining polynomial coefficients", "minimum": 0, "title": "Max Solver Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.max_solver_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "value" ], "title": "PceExpansionOrderCollocRatioConfig", "type": "object" }, "PceExpansionOrderCollocRatioLasso": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/ExpansionOrderCollocRatioLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "required": [ "least_absolute_shrinkage" ], "title": "PceExpansionOrderCollocRatioLasso", "type": "object" }, "PceExpansionOrderCollocRatioLeastSquares": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using least squares", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/ExpansionOrderCollocRatioLeastSquaresSvd" }, { "$ref": "#/$defs/ExpansionOrderCollocRatioLeastSquaresEqCon" }, { "additionalProperties": true, "type": "object" } ], "description": "Compute the coefficients of a polynomial expansion using least squares", "title": "Least Squares", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEFAULT_LEAST_SQ_REGRESSION" } ] } }, "required": [ "least_squares" ], "title": "PceExpansionOrderCollocRatioLeastSquares", "type": "object" }, "PceExpansionOrderCollocRatioOMP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/ExpansionOrderCollocRatioOMPConfig", "x-aliases": [ "omp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_MATCH_PURSUIT" } ] } }, "required": [ "orthogonal_matching_pursuit" ], "title": "PceExpansionOrderCollocRatioOMP", "type": "object" }, "PceExpansionOrderExpansionSamples": { "additionalProperties": false, "description": "Number of simulation samples used to estimate the expected value of a set of PCE coefficients", "properties": { "expansion_samples": { "$ref": "#/$defs/PceExpansionOrderExpansionSamplesConfig", "argument": "value" } }, "required": [ "expansion_samples" ], "title": "PceExpansionOrderExpansionSamples", "type": "object" }, "PceExpansionOrderExpansionSamplesConfig": { "additionalProperties": false, "description": "Number of simulation samples used to estimate the expected value of a set of PCE coefficients", "properties": { "value": { "default": 9223372036854775807, "description": "Number of simulation samples used to estimate the expected value of a set of PCE coefficients", "title": "Value", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.expansion_samples", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] } }, "title": "PceExpansionOrderExpansionSamplesConfig", "type": "object" }, "PceExpansionOrderImportBuildPointsFile": { "additionalProperties": false, "description": "File containing points you wish to use to build a surrogate", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "PceExpansionOrderImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "PceExpansionOrderImportBuildPointsFile", "type": "object" }, "PceExpansionOrderImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileAnnotated", "type": "object" }, "PceExpansionOrderImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotated", "type": "object" }, "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceExpansionOrderImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "point_selection" ] }
- Fields:
- field basis_type: PceExpansionOrderBasisTypeTensorProduct | PceExpansionOrderBasisTypeTotalOrder | PceExpansionOrderBasisTypeAdapted | None = None
Specify the type of basis truncation to be used for a Polynomial Chaos Expansion.
- field dimension_preference: list[DakotaFloat] | None = None
A set of weights specifying the realtive importance of each uncertain variable (dimension)
- field import_build_points_file: PceExpansionOrderImportBuildPointsFile | None = None
File containing points you wish to use to build a surrogate
- field order: int = 65535
The (initial) order of a polynomial expansion
- field point_selection: PceExpansionOrderCollocPoints | PceExpansionOrderCollocRatio | PceExpansionOrderExpansionSamples [Required]
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderExpansionSamples
Number of simulation samples used to estimate the expected value of a set of PCE coefficients
Show JSON schema
{ "title": "PceExpansionOrderExpansionSamples", "description": "Number of simulation samples used to estimate the expected value of a set of PCE coefficients", "type": "object", "properties": { "expansion_samples": { "$ref": "#/$defs/PceExpansionOrderExpansionSamplesConfig", "argument": "value" } }, "$defs": { "PceExpansionOrderExpansionSamplesConfig": { "additionalProperties": false, "description": "Number of simulation samples used to estimate the expected value of a set of PCE coefficients", "properties": { "value": { "default": 9223372036854775807, "description": "Number of simulation samples used to estimate the expected value of a set of PCE coefficients", "title": "Value", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.expansion_samples", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] } }, "title": "PceExpansionOrderExpansionSamplesConfig", "type": "object" } }, "additionalProperties": false, "required": [ "expansion_samples" ] }
- Fields:
- field expansion_samples: PceExpansionOrderExpansionSamplesConfig [Required]
Number of simulation samples used to estimate the expected value of a set of PCE coefficients
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderExpansionSamplesConfig
Number of simulation samples used to estimate the expected value of a set of PCE coefficients
Show JSON schema
{ "title": "PceExpansionOrderExpansionSamplesConfig", "description": "Number of simulation samples used to estimate the expected value of a set of PCE coefficients", "type": "object", "properties": { "value": { "default": 9223372036854775807, "description": "Number of simulation samples used to estimate the expected value of a set of PCE coefficients", "title": "Value", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.expansion_samples", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] } }, "additionalProperties": false }
- field reuse_points: Literal[True] | None = None
This describes the behavior of reuse of points in constructing polynomial chaos expansion models.
- field value: int = 9223372036854775807
Number of simulation samples used to estimate the expected value of a set of PCE coefficients
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderImportBuildPointsFile
File containing points you wish to use to build a surrogate
Show JSON schema
{ "title": "PceExpansionOrderImportBuildPointsFile", "description": "File containing points you wish to use to build a surrogate", "type": "object", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "PceExpansionOrderImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "$defs": { "PceExpansionOrderImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileAnnotated", "type": "object" }, "PceExpansionOrderImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotated", "type": "object" }, "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceExpansionOrderImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "filename" ] }
- Fields:
- field active_only: Literal[True] | None = None
Import only active variables from tabular data file
- field filename: str [Required]
File containing points you wish to use to build a surrogate
- field format: PceExpansionOrderImportBuildPointsFileCustomAnnotated | PceExpansionOrderImportBuildPointsFileAnnotated | PceExpansionOrderImportBuildPointsFileFreeform [Optional]
Tabular Format
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderImportBuildPointsFileAnnotated
Selects annotated tabular file format
Show JSON schema
{ "title": "PceExpansionOrderImportBuildPointsFileAnnotated", "description": "Selects annotated tabular file format", "type": "object", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "additionalProperties": false }
- Fields:
- field annotated: Literal[True] = True
Selects annotated tabular file format
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderImportBuildPointsFileCustomAnnotated
Selects custom-annotated tabular file format
Show JSON schema
{ "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotated", "description": "Selects custom-annotated tabular file format", "type": "object", "properties": { "custom_annotated": { "$ref": "#/$defs/PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig" } }, "$defs": { "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "type": "object" } }, "additionalProperties": false }
- field custom_annotated: PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig [Optional]
Selects custom-annotated tabular file format
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig
Selects custom-annotated tabular file format
Show JSON schema
{ "title": "PceExpansionOrderImportBuildPointsFileCustomAnnotatedConfig", "description": "Selects custom-annotated tabular file format", "type": "object", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "additionalProperties": false }
- Fields:
- field eval_id: Literal[True] | None = None
Enable evaluation ID column in custom-annotated tabular file
- field header: Literal[True] | None = None
Enable header row in custom-annotated tabular file
- field interface_id: Literal[True] | None = None
Enable interface ID column in custom-annotated tabular file
- pydantic model dakota.spec.method.polynomial_chaos.PceExpansionOrderImportBuildPointsFileFreeform
Selects freeform file format
Show JSON schema
{ "title": "PceExpansionOrderImportBuildPointsFileFreeform", "description": "Selects freeform file format", "type": "object", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "additionalProperties": false }
- Fields:
- field freeform: Literal[True] = True
Selects freeform file format
- pydantic model dakota.spec.method.polynomial_chaos.PceImportExpansionFile
Build a Polynomial Chaos Expansion (PCE) by importing expansion coefficients and a corresponding multi-index from a file
Show JSON schema
{ "title": "PceImportExpansionFile", "description": "Build a Polynomial Chaos Expansion (PCE) by importing expansion coefficients and a corresponding multi-index from a file", "type": "object", "properties": { "import_expansion_file": { "description": "Build a Polynomial Chaos Expansion (PCE) by importing expansion coefficients and a corresponding multi-index from a file", "title": "Import Expansion File", "type": "string", "x-materialization": [ { "ir_key": "method.nond.import_expansion_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false, "required": [ "import_expansion_file" ] }
- Fields:
- field import_expansion_file: str [Required]
Build a Polynomial Chaos Expansion (PCE) by importing expansion coefficients and a corresponding multi-index from a file
- pydantic model dakota.spec.method.polynomial_chaos.PceOrthogLeastInterp
Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.
Show JSON schema
{ "title": "PceOrthogLeastInterp", "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "type": "object", "properties": { "orthogonal_least_interpolation": { "$ref": "#/$defs/PceOrthogLeastInterpConfig", "x-aliases": [ "least_interpolation", "oli" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_LEAST_INTERPOLATION" } ] } }, "$defs": { "PceOrthogLeastInterpConfig": { "additionalProperties": false, "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "properties": { "collocation_points": { "description": "Number of collocation points used to estimate expansion coefficients", "title": "Collocation Points", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.collocation_points", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "tensor_grid": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.", "title": "Tensor Grid", "x-materialization": [ { "ir_key": "method.nond.tensor_grid_order", "ir_value_type": "UShortArray", "storage_type": "DIRECT_VALUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] }, "import_build_points_file": { "anyOf": [ { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] } }, "required": [ "collocation_points" ], "title": "PceOrthogLeastInterpConfig", "type": "object", "x-model-validations": [ { "validationContext": "pceorthogleastinterpconfig", "validationErrorMessage": "For pceorthogleastinterpconfig, all elements of tensor_grid must be >= 0.", "validationFields": [ "tensor_grid" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "PceOrthogLeastInterpImportBuildPointsFile": { "additionalProperties": false, "description": "File containing points you wish to use to build a surrogate", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "PceOrthogLeastInterpImportBuildPointsFile", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "orthogonal_least_interpolation" ] }
- Fields:
- field orthogonal_least_interpolation: PceOrthogLeastInterpConfig [Required]
Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.
- pydantic model dakota.spec.method.polynomial_chaos.PceOrthogLeastInterpConfig
Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.
Show JSON schema
{ "title": "PceOrthogLeastInterpConfig", "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "type": "object", "properties": { "collocation_points": { "description": "Number of collocation points used to estimate expansion coefficients", "title": "Collocation Points", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.collocation_points", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "tensor_grid": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.", "title": "Tensor Grid", "x-materialization": [ { "ir_key": "method.nond.tensor_grid_order", "ir_value_type": "UShortArray", "storage_type": "DIRECT_VALUE" } ] }, "reuse_points": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "This describes the behavior of reuse of points in constructing polynomial chaos expansion models.", "title": "Reuse Points", "x-aliases": [ "reuse_samples" ], "x-materialization": [ { "ir_key": "method.nond.point_reuse", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "all" } ] }, "import_build_points_file": { "anyOf": [ { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] } }, "$defs": { "PceOrthogLeastInterpImportBuildPointsFile": { "additionalProperties": false, "description": "File containing points you wish to use to build a surrogate", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "PceOrthogLeastInterpImportBuildPointsFile", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "collocation_points" ], "x-model-validations": [ { "validationContext": "pceorthogleastinterpconfig", "validationErrorMessage": "For pceorthogleastinterpconfig, all elements of tensor_grid must be >= 0.", "validationFields": [ "tensor_grid" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }
- Fields:
- field collocation_points: int [Required]
Number of collocation points used to estimate expansion coefficients
- field import_build_points_file: PceOrthogLeastInterpImportBuildPointsFile | None = None
File containing points you wish to use to build a surrogate
- field reuse_points: Literal[True] | None = None
This describes the behavior of reuse of points in constructing polynomial chaos expansion models.
- field tensor_grid: list[int] | None = None
Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.
- pydantic model dakota.spec.method.polynomial_chaos.PceOrthogLeastInterpImportBuildPointsFile
File containing points you wish to use to build a surrogate
Show JSON schema
{ "title": "PceOrthogLeastInterpImportBuildPointsFile", "description": "File containing points you wish to use to build a surrogate", "type": "object", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "$defs": { "PceOrthogLeastInterpImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "PceOrthogLeastInterpImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "filename" ] }
- Fields:
- field active_only: Literal[True] | None = None
Import only active variables from tabular data file
- field filename: str [Required]
File containing points you wish to use to build a surrogate
- field format: PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated | PceOrthogLeastInterpImportBuildPointsFileAnnotated | PceOrthogLeastInterpImportBuildPointsFileFreeform [Optional]
Tabular Format
- pydantic model dakota.spec.method.polynomial_chaos.PceOrthogLeastInterpImportBuildPointsFileAnnotated
Selects annotated tabular file format
Show JSON schema
{ "title": "PceOrthogLeastInterpImportBuildPointsFileAnnotated", "description": "Selects annotated tabular file format", "type": "object", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "additionalProperties": false }
- Fields:
- field annotated: Literal[True] = True
Selects annotated tabular file format
- pydantic model dakota.spec.method.polynomial_chaos.PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated
Selects custom-annotated tabular file format
Show JSON schema
{ "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "description": "Selects custom-annotated tabular file format", "type": "object", "properties": { "custom_annotated": { "$ref": "#/$defs/PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "$defs": { "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" } }, "additionalProperties": false }
- field custom_annotated: PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig [Optional]
Selects custom-annotated tabular file format
- pydantic model dakota.spec.method.polynomial_chaos.PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig
Selects custom-annotated tabular file format
Show JSON schema
{ "title": "PceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "description": "Selects custom-annotated tabular file format", "type": "object", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "additionalProperties": false }
- Fields:
- field eval_id: Literal[True] | None = None
Enable evaluation ID column in custom-annotated tabular file
- field header: Literal[True] | None = None
Enable header row in custom-annotated tabular file
- field interface_id: Literal[True] | None = None
Enable interface ID column in custom-annotated tabular file
- pydantic model dakota.spec.method.polynomial_chaos.PceOrthogLeastInterpImportBuildPointsFileFreeform
Selects freeform file format
Show JSON schema
{ "title": "PceOrthogLeastInterpImportBuildPointsFileFreeform", "description": "Selects freeform file format", "type": "object", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "additionalProperties": false }
- Fields:
- field freeform: Literal[True] = True
Selects freeform file format
- pydantic model dakota.spec.method.polynomial_chaos.PceQuadratureOrder
Order for tensor-products of Gaussian quadrature rules
Show JSON schema
{ "title": "PceQuadratureOrder", "description": "Order for tensor-products of Gaussian quadrature rules", "type": "object", "properties": { "quadrature_order": { "$ref": "#/$defs/PceQuadratureOrderConfig", "argument": "order" } }, "$defs": { "PceQuadratureOrderConfig": { "additionalProperties": false, "description": "Order for tensor-products of Gaussian quadrature rules", "properties": { "order": { "default": 65535, "description": "Order for tensor-products of Gaussian quadrature rules", "title": "Order", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.quadrature_order", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "dimension_preference": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "A set of weights specifying the realtive importance of each uncertain variable (dimension)", "title": "Dimension Preference", "x-materialization": [ { "ir_key": "method.nond.dimension_preference", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "nesting_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceQuadratureOrderNested" }, { "$ref": "#/$defs/PceQuadratureOrderNonNested" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Nesting", "title": "Nesting Rule", "x-union-pattern": 2 } }, "title": "PceQuadratureOrderConfig", "type": "object" }, "PceQuadratureOrderNested": { "additionalProperties": false, "description": "Enforce use of nested quadrature rules if available", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "title": "PceQuadratureOrderNested", "type": "object" }, "PceQuadratureOrderNonNested": { "additionalProperties": false, "description": "Enforce use of non-nested quadrature rules", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "title": "PceQuadratureOrderNonNested", "type": "object" } }, "additionalProperties": false, "required": [ "quadrature_order" ] }
- field quadrature_order: PceQuadratureOrderConfig [Required]
Order for tensor-products of Gaussian quadrature rules
- pydantic model dakota.spec.method.polynomial_chaos.PceQuadratureOrderConfig
Order for tensor-products of Gaussian quadrature rules
Show JSON schema
{ "title": "PceQuadratureOrderConfig", "description": "Order for tensor-products of Gaussian quadrature rules", "type": "object", "properties": { "order": { "default": 65535, "description": "Order for tensor-products of Gaussian quadrature rules", "title": "Order", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.quadrature_order", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "dimension_preference": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "A set of weights specifying the realtive importance of each uncertain variable (dimension)", "title": "Dimension Preference", "x-materialization": [ { "ir_key": "method.nond.dimension_preference", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "nesting_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceQuadratureOrderNested" }, { "$ref": "#/$defs/PceQuadratureOrderNonNested" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Nesting", "title": "Nesting Rule", "x-union-pattern": 2 } }, "$defs": { "PceQuadratureOrderNested": { "additionalProperties": false, "description": "Enforce use of nested quadrature rules if available", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "title": "PceQuadratureOrderNested", "type": "object" }, "PceQuadratureOrderNonNested": { "additionalProperties": false, "description": "Enforce use of non-nested quadrature rules", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "title": "PceQuadratureOrderNonNested", "type": "object" } }, "additionalProperties": false }
- Fields:
- field dimension_preference: list[DakotaFloat] | None = None
A set of weights specifying the realtive importance of each uncertain variable (dimension)
- field nesting_rule: PceQuadratureOrderNested | PceQuadratureOrderNonNested | None = None
Quadrature Rule Nesting
- field order: int = 65535
Order for tensor-products of Gaussian quadrature rules
- pydantic model dakota.spec.method.polynomial_chaos.PceQuadratureOrderNested
Enforce use of nested quadrature rules if available
Show JSON schema
{ "title": "PceQuadratureOrderNested", "description": "Enforce use of nested quadrature rules if available", "type": "object", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "additionalProperties": false }
- Fields:
- field nested: Literal[True] = True
Enforce use of nested quadrature rules if available
- pydantic model dakota.spec.method.polynomial_chaos.PceQuadratureOrderNonNested
Enforce use of non-nested quadrature rules
Show JSON schema
{ "title": "PceQuadratureOrderNonNested", "description": "Enforce use of non-nested quadrature rules", "type": "object", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "additionalProperties": false }
- Fields:
- field non_nested: Literal[True] = True
Enforce use of non-nested quadrature rules
- pydantic model dakota.spec.method.polynomial_chaos.PceSGLevel
Level to use in sparse grid integration or interpolation
Show JSON schema
{ "title": "PceSGLevel", "description": "Level to use in sparse grid integration or interpolation", "type": "object", "properties": { "sparse_grid_level": { "$ref": "#/$defs/PceSGLevelConfig", "argument": "level" } }, "$defs": { "PceSGLevelConfig": { "additionalProperties": false, "description": "Level to use in sparse grid integration or interpolation", "properties": { "level": { "default": 65535, "description": "Level to use in sparse grid integration or interpolation", "title": "Level", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.sparse_grid_level", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "dimension_preference": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "A set of weights specifying the realtive importance of each uncertain variable (dimension)", "title": "Dimension Preference", "x-materialization": [ { "ir_key": "method.nond.dimension_preference", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "growth_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceSGLevelRestricted" }, { "$ref": "#/$defs/PceSGLevelUnrestricted" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Growth", "title": "Growth Rule", "x-union-pattern": 2 }, "nesting_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceSGLevelNested" }, { "$ref": "#/$defs/PceSGLevelNonNested" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Nesting", "title": "Nesting Rule", "x-union-pattern": 2 } }, "title": "PceSGLevelConfig", "type": "object" }, "PceSGLevelNested": { "additionalProperties": false, "description": "Enforce use of nested quadrature rules if available", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "title": "PceSGLevelNested", "type": "object" }, "PceSGLevelNonNested": { "additionalProperties": false, "description": "Enforce use of non-nested quadrature rules", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "title": "PceSGLevelNonNested", "type": "object" }, "PceSGLevelRestricted": { "additionalProperties": false, "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "properties": { "restricted": { "const": true, "default": true, "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "title": "Restricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RESTRICTED" } ] } }, "title": "PceSGLevelRestricted", "type": "object" }, "PceSGLevelUnrestricted": { "additionalProperties": false, "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "properties": { "unrestricted": { "const": true, "default": true, "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "title": "Unrestricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "UNRESTRICTED" } ] } }, "title": "PceSGLevelUnrestricted", "type": "object" } }, "additionalProperties": false, "required": [ "sparse_grid_level" ] }
- field sparse_grid_level: PceSGLevelConfig [Required]
Level to use in sparse grid integration or interpolation
- pydantic model dakota.spec.method.polynomial_chaos.PceSGLevelConfig
Level to use in sparse grid integration or interpolation
Show JSON schema
{ "title": "PceSGLevelConfig", "description": "Level to use in sparse grid integration or interpolation", "type": "object", "properties": { "level": { "default": 65535, "description": "Level to use in sparse grid integration or interpolation", "title": "Level", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.sparse_grid_level", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "dimension_preference": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "A set of weights specifying the realtive importance of each uncertain variable (dimension)", "title": "Dimension Preference", "x-materialization": [ { "ir_key": "method.nond.dimension_preference", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "growth_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceSGLevelRestricted" }, { "$ref": "#/$defs/PceSGLevelUnrestricted" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Growth", "title": "Growth Rule", "x-union-pattern": 2 }, "nesting_rule": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/PceSGLevelNested" }, { "$ref": "#/$defs/PceSGLevelNonNested" }, { "type": "null" } ], "default": null, "description": "Quadrature Rule Nesting", "title": "Nesting Rule", "x-union-pattern": 2 } }, "$defs": { "PceSGLevelNested": { "additionalProperties": false, "description": "Enforce use of nested quadrature rules if available", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "title": "PceSGLevelNested", "type": "object" }, "PceSGLevelNonNested": { "additionalProperties": false, "description": "Enforce use of non-nested quadrature rules", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "title": "PceSGLevelNonNested", "type": "object" }, "PceSGLevelRestricted": { "additionalProperties": false, "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "properties": { "restricted": { "const": true, "default": true, "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "title": "Restricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RESTRICTED" } ] } }, "title": "PceSGLevelRestricted", "type": "object" }, "PceSGLevelUnrestricted": { "additionalProperties": false, "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "properties": { "unrestricted": { "const": true, "default": true, "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "title": "Unrestricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "UNRESTRICTED" } ] } }, "title": "PceSGLevelUnrestricted", "type": "object" } }, "additionalProperties": false }
- Fields:
- field dimension_preference: list[DakotaFloat] | None = None
A set of weights specifying the realtive importance of each uncertain variable (dimension)
- field growth_rule: PceSGLevelRestricted | PceSGLevelUnrestricted | None = None
Quadrature Rule Growth
- field level: int = 65535
Level to use in sparse grid integration or interpolation
- field nesting_rule: PceSGLevelNested | PceSGLevelNonNested | None = None
Quadrature Rule Nesting
- pydantic model dakota.spec.method.polynomial_chaos.PceSGLevelNested
Enforce use of nested quadrature rules if available
Show JSON schema
{ "title": "PceSGLevelNested", "description": "Enforce use of nested quadrature rules if available", "type": "object", "properties": { "nested": { "const": true, "default": true, "description": "Enforce use of nested quadrature rules if available", "title": "Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NESTED" } ] } }, "additionalProperties": false }
- Fields:
- field nested: Literal[True] = True
Enforce use of nested quadrature rules if available
- pydantic model dakota.spec.method.polynomial_chaos.PceSGLevelNonNested
Enforce use of non-nested quadrature rules
Show JSON schema
{ "title": "PceSGLevelNonNested", "description": "Enforce use of non-nested quadrature rules", "type": "object", "properties": { "non_nested": { "const": true, "default": true, "description": "Enforce use of non-nested quadrature rules", "title": "Non Nested", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.nesting_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NON_NESTED" } ] } }, "additionalProperties": false }
- Fields:
- field non_nested: Literal[True] = True
Enforce use of non-nested quadrature rules
- pydantic model dakota.spec.method.polynomial_chaos.PceSGLevelRestricted
Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.
Show JSON schema
{ "title": "PceSGLevelRestricted", "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "type": "object", "properties": { "restricted": { "const": true, "default": true, "description": "Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.", "title": "Restricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RESTRICTED" } ] } }, "additionalProperties": false }
- Fields:
- field restricted: Literal[True] = True
Restrict the growth rates for nested and non-nested rules can be synchronized for consistency.
- pydantic model dakota.spec.method.polynomial_chaos.PceSGLevelUnrestricted
Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.
Show JSON schema
{ "title": "PceSGLevelUnrestricted", "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "type": "object", "properties": { "unrestricted": { "const": true, "default": true, "description": "Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.", "title": "Unrestricted", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.growth_override", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "UNRESTRICTED" } ] } }, "additionalProperties": false }
- Fields:
- field unrestricted: Literal[True] = True
Overide the default restriction of growth rates for nested and non-nested rules that are by defualt synchronized for consistency.
