multilevel_polynomial_chaos
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceSelection
Generated model for MlPceSelection
Show JSON schema
{ "title": "MlPceSelection", "description": "Generated model for MlPceSelection", "type": "object", "properties": { "multilevel_polynomial_chaos": { "$ref": "#/$defs/MlPceConfig", "x-materialization": [ { "ir_key": "method.algorithm", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "MULTILEVEL_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" }, "DiscrepEmulationDiscrepancyEmulationDistinct": { "additionalProperties": false, "description": "Distinct formulation for emulation of model discrepancies.", "properties": { "distinct": { "const": true, "default": true, "description": "Distinct formulation for emulation of model discrepancies.", "title": "Distinct", "type": "boolean", "x-aliases": [ "paired" ], "x-materialization": [ { "ir_key": "method.nond.multilevel_discrepancy_emulation", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DISTINCT_EMULATION" } ] } }, "title": "DiscrepEmulationDiscrepancyEmulationDistinct", "type": "object" }, "DiscrepEmulationDiscrepancyEmulationRecursive": { "additionalProperties": false, "description": "Recursive formulation for emulation of model discrepancies.", "properties": { "recursive": { "const": true, "default": true, "description": "Recursive formulation for emulation of model discrepancies.", "title": "Recursive", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.multilevel_discrepancy_emulation", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RECURSIVE_EMULATION" } ] } }, "title": "DiscrepEmulationDiscrepancyEmulationRecursive", "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" }, "ImportApproxPointsFile": { "additionalProperties": false, "description": "Filename for points at which to evaluate the PCE/SC surrogate", "properties": { "filename": { "description": "Filename for points at which to evaluate the PCE/SC surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_approx_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ImportApproxPointsFileCustomAnnotated" }, { "$ref": "#/$defs/ImportApproxPointsFileAnnotated" }, { "$ref": "#/$defs/ImportApproxPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "ImportApproxPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_approx_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "ImportApproxPointsFile", "type": "object" }, "ImportApproxPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "ImportApproxPointsFileAnnotated", "type": "object" }, "ImportApproxPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/ImportApproxPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "ImportApproxPointsFileCustomAnnotatedConfig" } }, "title": "ImportApproxPointsFileCustomAnnotated", "type": "object" }, "ImportApproxPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated 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"#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileFreeform": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileFreeform", "type": "object" }, "MlpceAllocControlAllocationControlEstimatorVariance": { "additionalProperties": false, "description": "Variance of mean estimator within multilevel polynomial chaos", "properties": { "estimator_variance": { "$ref": "#/$defs/MlpceAllocControlAllocationControlEstimatorVarianceConfig", "x-materialization": [ { "ir_key": "method.nond.multilevel_allocation_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ESTIMATOR_VARIANCE" } ] } }, "required": [ "estimator_variance" ], "title": "MlpceAllocControlAllocationControlEstimatorVariance", "type": "object" }, "MlpceAllocControlAllocationControlEstimatorVarianceConfig": { "additionalProperties": false, "description": "Variance of mean estimator within multilevel polynomial chaos", "properties": { "estimator_rate": { "default": 2.0, "description": "Rate of convergence of mean estimator within multilevel polynomial chaos", "title": "Estimator Rate", "type": "number", "x-materialization": [ { "ir_key": "method.nond.multilevel_estimator_rate", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "MlpceAllocControlAllocationControlEstimatorVarianceConfig", "type": "object" }, "MlpceAllocControlAllocationControlRipSampling": { "additionalProperties": false, "description": "Sample allocation based on restricted isometry property (RIP) within multilevel polynomial chaos", "properties": { "rip_sampling": { "const": true, "default": true, "description": "Sample allocation based on restricted isometry property (RIP) within multilevel polynomial chaos", "title": "Rip Sampling", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.multilevel_allocation_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RIP_SAMPLING" } ] } }, "title": "MlpceAllocControlAllocationControlRipSampling", "type": "object" }, "Normal": { "additionalProperties": false, "description": "Level 3 of 5 - default", "properties": { "normal": { "const": true, "default": true, "description": "Level 3 of 5 - default", "title": "Normal", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NORMAL_OUTPUT" } ] } }, "title": "Normal", "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" }, "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": [ "multilevel_polynomial_chaos" ] }
- field multilevel_polynomial_chaos: MlPceConfig [Required]
- 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.multilevel_polynomial_chaos.MlPceConfig
Multilevel uncertainty quantification using polynomial chaos expansions
Show JSON schema
{ "title": "MlPceConfig", "description": "Multilevel 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_sequence": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Sequence of seed values for multi-stage random sampling", "title": "Seed Sequence", "x-materialization": [ { "ir_key": "method.random_seed_sequence", "ir_value_type": "SizetArray", "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 }, "discrepancy_emulation": { "anyOf": [ { "$ref": "#/$defs/DiscrepEmulationDiscrepancyEmulationDistinct" }, { "$ref": "#/$defs/DiscrepEmulationDiscrepancyEmulationRecursive" }, { "type": "null" } ], "default": null, "description": "Formulation for emulation of model discrepancies.", "title": "Discrepancy Emulation", "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" }, "allocation_control": { "anyOf": [ { "$ref": "#/$defs/MlpceAllocControlAllocationControlEstimatorVariance" }, { "$ref": "#/$defs/MlpceAllocControlAllocationControlRipSampling" }, { "type": "null" } ], "default": null, "description": "Sample allocation approach for multilevel expansions", "title": "Allocation Control", "x-union-pattern": 2 }, "max_iterations": { "default": 9223372036854775807, "description": "Number of iterations allowed for optimizers and adaptive UQ methods", "minimum": 0, "title": "Max Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_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/MlPceExpansionOrderSequence" }, { "$ref": "#/$defs/MlPceOrthogLeastInterp" } ], "description": "Coefficient Computation 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" }, "DiscrepEmulationDiscrepancyEmulationDistinct": { "additionalProperties": false, "description": "Distinct formulation for emulation of model discrepancies.", "properties": { "distinct": { "const": true, "default": true, "description": "Distinct formulation for emulation of model discrepancies.", "title": "Distinct", "type": "boolean", "x-aliases": [ "paired" ], "x-materialization": [ { "ir_key": "method.nond.multilevel_discrepancy_emulation", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DISTINCT_EMULATION" } ] } }, "title": "DiscrepEmulationDiscrepancyEmulationDistinct", "type": "object" }, "DiscrepEmulationDiscrepancyEmulationRecursive": { "additionalProperties": false, "description": "Recursive formulation for emulation of model discrepancies.", "properties": { "recursive": { "const": true, "default": true, "description": "Recursive formulation for emulation of model discrepancies.", "title": "Recursive", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.multilevel_discrepancy_emulation", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RECURSIVE_EMULATION" } ] } }, "title": "DiscrepEmulationDiscrepancyEmulationRecursive", "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" } ] }, 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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" }, "ImportApproxPointsFile": { "additionalProperties": false, "description": "Filename for points at which to evaluate the PCE/SC surrogate", "properties": { "filename": { "description": "Filename for points at which to evaluate the PCE/SC surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_approx_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ImportApproxPointsFileCustomAnnotated" }, { "$ref": 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"ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "ImportApproxPointsFileAnnotated", "type": "object" }, "ImportApproxPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/ImportApproxPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "ImportApproxPointsFileCustomAnnotatedConfig" } }, "title": "ImportApproxPointsFileCustomAnnotated", "type": "object" }, "ImportApproxPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": 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"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": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "MlPceExpansionOrderSequenceImportBuildPointsFileFreeform": { "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": "MlPceExpansionOrderSequenceImportBuildPointsFileFreeform", "type": "object" }, "MlPceOrthogLeastInterp": { "additionalProperties": false, "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "properties": { "orthogonal_least_interpolation": { "$ref": "#/$defs/MlPceOrthogLeastInterpConfig", "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": "MlPceOrthogLeastInterp", "type": "object" }, "MlPceOrthogLeastInterpConfig": { "additionalProperties": false, "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "properties": { "collocation_points_sequence": { "description": "Sequence of collocation point counts used in a multi-stage expansion", "items": { "type": "integer" }, "title": "Collocation Points Sequence", "type": "array", "x-materialization": [ { "ir_key": "method.nond.collocation_points_sequence", "ir_value_type": "SizetArray", "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/MlPceOrthogLeastInterpImportBuildPointsFile" }, { "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_sequence" ], "title": "MlPceOrthogLeastInterpConfig", "type": "object", "x-model-validations": [ { "validationContext": "mlpceorthogleastinterpconfig", "validationErrorMessage": "For mlpceorthogleastinterpconfig, all elements of collocation_points_sequence must be >= 0.", "validationFields": [ "collocation_points_sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" }, { "validationContext": "mlpceorthogleastinterpconfig", "validationErrorMessage": "For mlpceorthogleastinterpconfig, all elements of tensor_grid must be >= 0.", "validationFields": [ "tensor_grid" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "MlPceOrthogLeastInterpImportBuildPointsFile": { "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/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated", "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": "MlPceOrthogLeastInterpImportBuildPointsFile", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileFreeform": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileFreeform", "type": "object" }, "MlpceAllocControlAllocationControlEstimatorVariance": { "additionalProperties": false, "description": "Variance of mean estimator within multilevel polynomial chaos", "properties": { "estimator_variance": { "$ref": "#/$defs/MlpceAllocControlAllocationControlEstimatorVarianceConfig", "x-materialization": [ { "ir_key": "method.nond.multilevel_allocation_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ESTIMATOR_VARIANCE" } ] } }, "required": [ "estimator_variance" ], "title": "MlpceAllocControlAllocationControlEstimatorVariance", "type": "object" }, "MlpceAllocControlAllocationControlEstimatorVarianceConfig": { "additionalProperties": false, "description": "Variance of mean estimator within multilevel polynomial chaos", "properties": { "estimator_rate": { "default": 2.0, "description": "Rate of convergence of mean estimator within multilevel polynomial chaos", "title": "Estimator Rate", "type": "number", "x-materialization": [ { "ir_key": "method.nond.multilevel_estimator_rate", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "MlpceAllocControlAllocationControlEstimatorVarianceConfig", "type": "object" }, "MlpceAllocControlAllocationControlRipSampling": { "additionalProperties": false, "description": "Sample allocation based on restricted isometry property (RIP) within multilevel polynomial chaos", "properties": { "rip_sampling": { "const": true, "default": true, "description": "Sample allocation based on restricted isometry property (RIP) within multilevel polynomial chaos", "title": "Rip Sampling", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.multilevel_allocation_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RIP_SAMPLING" } ] } }, "title": "MlpceAllocControlAllocationControlRipSampling", "type": "object" }, "Normal": { "additionalProperties": false, "description": "Level 3 of 5 - default", "properties": { "normal": { "const": true, "default": true, "description": "Level 3 of 5 - default", "title": "Normal", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NORMAL_OUTPUT" } ] } }, "title": "Normal", "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" }, "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" ], "x-model-validations": [ { "validationContext": "methodseedsequencemixin", "validationErrorMessage": "For methodseedsequencemixin, all elements of seed_sequence must be >= 0.", "validationFields": [ "seed_sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }
- 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 allocation_control: MlpceAllocControlAllocationControlEstimatorVariance | MlpceAllocControlAllocationControlRipSampling | None = None
Sample allocation approach for multilevel expansions
- field basis_family: PceOptionsAskey | PceOptionsWiener | None = None
Basis Polynomial Family
- field coefficient_approach: MlPceExpansionOrderSequence | MlPceOrthogLeastInterp [Required]
Coefficient Computation 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 discrepancy_emulation: DiscrepEmulationDiscrepancyEmulationDistinct | DiscrepEmulationDiscrepancyEmulationRecursive | None = None
Formulation for emulation of model discrepancies.
- 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_iterations: int = 9223372036854775807
Number of iterations allowed for optimizers and adaptive UQ methods
- 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 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_sequence: list[int] | None = None
Sequence of seed values for multi-stage random sampling
- 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.multilevel_polynomial_chaos
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequence
Sequence of expansion orders used in a multi-stage expansion
Show JSON schema
{ "title": "MlPceExpansionOrderSequence", "description": "Sequence of expansion orders used in a multi-stage expansion", "type": "object", "properties": { "expansion_order_sequence": { "$ref": "#/$defs/MlPceExpansionOrderSequenceConfig", "argument": "sequence" } }, "$defs": { "MlPceExpansionOrderSequenceBasisTypeAdapted": { "additionalProperties": false, "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "properties": { "adapted": { "$ref": "#/$defs/MlPceExpansionOrderSequenceBasisTypeAdaptedConfig", "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": "MlPceExpansionOrderSequenceBasisTypeAdapted", "type": "object" }, "MlPceExpansionOrderSequenceBasisTypeAdaptedConfig": { "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": "MlPceExpansionOrderSequenceBasisTypeAdaptedConfig", "type": "object" }, "MlPceExpansionOrderSequenceBasisTypeTensorProduct": { "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": "MlPceExpansionOrderSequenceBasisTypeTensorProduct", "type": "object" }, "MlPceExpansionOrderSequenceBasisTypeTotalOrder": { "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": "MlPceExpansionOrderSequenceBasisTypeTotalOrder", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatio": { "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": "#/$defs/MlPceExpansionOrderSequenceCollocRatioConfig", "argument": "sequence" } }, "required": [ "collocation_ratio" ], "title": "MlPceExpansionOrderSequenceCollocRatio", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioBP", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "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": "MlPceExpansionOrderSequenceCollocRatioBPDN", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBPDNConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioCV": { "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": "MlPceExpansionOrderSequenceCollocRatioCV", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioConfig": { "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": { "sequence": { "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": "Sequence", "type": "number", "x-materialization": [ { "ir_key": "method.nond.collocation_ratio", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "collocation_points_sequence": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Sequence of collocation point counts used in a multi-stage expansion", "title": "Collocation Points Sequence", "x-aliases": [ "pilot_samples" ], "x-materialization": [ { "ir_key": "method.nond.collocation_points_sequence", "ir_value_type": "SizetArray", "storage_type": "DIRECT_VALUE" } ] }, "regression_method": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquares" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioOMP" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBP" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBPDN" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLars" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLasso" }, { "type": "null" } ], "default": null, "description": "Regression Algorithm", "title": "Regression Method", "x-union-pattern": 2 }, "cross_validation": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioCV" }, { "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": [ "sequence" ], "title": "MlPceExpansionOrderSequenceCollocRatioConfig", "type": "object", "x-model-validations": [ { "validationContext": "mlpceexpansionordersequencecollocratioconfig", "validationErrorMessage": "For mlpceexpansionordersequencecollocratioconfig, all elements of collocation_points_sequence must be >= 0.", "validationFields": [ "collocation_points_sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "MlPceExpansionOrderSequenceCollocRatioLars": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "least_angle_regression": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "required": [ "least_angle_regression" ], "title": "MlPceExpansionOrderSequenceCollocRatioLars", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioLarsConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLasso": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "required": [ "least_absolute_shrinkage" ], "title": "MlPceExpansionOrderSequenceCollocRatioLasso", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioLassoConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquares": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using least squares", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon" }, { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquares", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioOMP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioOMPConfig", "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": "MlPceExpansionOrderSequenceCollocRatioOMP", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioOMPConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioOMPConfig", "type": "object" }, "MlPceExpansionOrderSequenceConfig": { "additionalProperties": false, "description": "Sequence of expansion orders used in a multi-stage expansion", "properties": { "sequence": { "description": "Sequence of expansion orders used in a multi-stage expansion", "items": { "type": "integer" }, "title": "Sequence", "type": "array", "x-materialization": [ { "ir_key": "method.nond.expansion_order_sequence", "ir_value_type": "UShortArray", "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": "#/$defs/MlPceExpansionOrderSequenceBasisTypeTensorProduct" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceBasisTypeTotalOrder" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceBasisTypeAdapted" }, { "type": "null" } ], "default": null, "description": "Specify the type of basis truncation to be used for a Polynomial Chaos Expansion.", "title": "Basis Type", "x-union-pattern": 2 }, "point_sequence_selection": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatio" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceExpansionSamplesSequence" } ], "title": "Point Sequence Selection", "x-union-pattern": 4 }, "import_build_points_file": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] } }, "required": [ "sequence", "point_sequence_selection" ], "title": "MlPceExpansionOrderSequenceConfig", "type": "object", "x-model-validations": [ { "validationContext": "mlpceexpansionordersequenceconfig", "validationErrorMessage": "For mlpceexpansionordersequenceconfig, all elements of sequence must be >= 0.", "validationFields": [ "sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "MlPceExpansionOrderSequenceExpansionSamplesSequence": { "additionalProperties": false, "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "properties": { "expansion_samples_sequence": { "$ref": "#/$defs/MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig", "argument": "sequence" } }, "required": [ "expansion_samples_sequence" ], "title": "MlPceExpansionOrderSequenceExpansionSamplesSequence", "type": "object" }, "MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig": { "additionalProperties": false, "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "properties": { "sequence": { "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "items": { "type": "integer" }, "title": "Sequence", "type": "array", "x-materialization": [ { "ir_key": "method.nond.expansion_samples_sequence", "ir_value_type": "SizetArray", "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" } ] } }, "required": [ "sequence" ], "title": "MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig", "type": "object", "x-model-validations": [ { "validationContext": "mlpceexpansionordersequenceexpansionsamplessequenceconfig", "validationErrorMessage": "For mlpceexpansionordersequenceexpansionsamplessequenceconfig, all elements of sequence must be >= 0.", "validationFields": [ "sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "MlPceExpansionOrderSequenceImportBuildPointsFile": { "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": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "MlPceExpansionOrderSequenceImportBuildPointsFile", "type": "object" }, "MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated", "type": "object" }, "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotated", "type": "object" }, "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig": { "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": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "MlPceExpansionOrderSequenceImportBuildPointsFileFreeform": { "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": "MlPceExpansionOrderSequenceImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "expansion_order_sequence" ] }
- field expansion_order_sequence: MlPceExpansionOrderSequenceConfig [Required]
Sequence of expansion orders used in a multi-stage expansion
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceBasisTypeAdapted
Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceBasisTypeAdapted", "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "type": "object", "properties": { "adapted": { "$ref": "#/$defs/MlPceExpansionOrderSequenceBasisTypeAdaptedConfig", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ADAPTED_BASIS_EXPANDING_FRONT" } ] } }, "$defs": { "MlPceExpansionOrderSequenceBasisTypeAdaptedConfig": { "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": "MlPceExpansionOrderSequenceBasisTypeAdaptedConfig", "type": "object" } }, "additionalProperties": false, "required": [ "adapted" ] }
- field adapted: MlPceExpansionOrderSequenceBasisTypeAdaptedConfig [Required]
Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceBasisTypeAdaptedConfig
Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceBasisTypeAdaptedConfig", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceBasisTypeTensorProduct
Use a tensor-product index set to construct a polynomial chaos expansion.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceBasisTypeTensorProduct", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceBasisTypeTotalOrder
Use a total-order index set to construct a polynomial chaos expansion.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceBasisTypeTotalOrder", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatio
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
{ "title": "MlPceExpansionOrderSequenceCollocRatio", "description": "Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.", "type": "object", "properties": { "collocation_ratio": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioConfig", "argument": "sequence" } }, "$defs": { "MlPceExpansionOrderSequenceCollocRatioBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioBP", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "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": "MlPceExpansionOrderSequenceCollocRatioBPDN", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBPDNConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioCV": { "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": "MlPceExpansionOrderSequenceCollocRatioCV", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioConfig": { "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": { "sequence": { "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": "Sequence", "type": "number", "x-materialization": [ { "ir_key": "method.nond.collocation_ratio", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "collocation_points_sequence": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Sequence of collocation point counts used in a multi-stage expansion", "title": "Collocation Points Sequence", "x-aliases": [ "pilot_samples" ], "x-materialization": [ { "ir_key": "method.nond.collocation_points_sequence", "ir_value_type": "SizetArray", "storage_type": "DIRECT_VALUE" } ] }, "regression_method": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquares" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioOMP" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBP" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBPDN" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLars" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLasso" }, { "type": "null" } ], "default": null, "description": "Regression Algorithm", "title": "Regression Method", "x-union-pattern": 2 }, "cross_validation": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioCV" }, { "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": [ "sequence" ], "title": "MlPceExpansionOrderSequenceCollocRatioConfig", "type": "object", "x-model-validations": [ { "validationContext": "mlpceexpansionordersequencecollocratioconfig", "validationErrorMessage": "For mlpceexpansionordersequencecollocratioconfig, all elements of collocation_points_sequence must be >= 0.", "validationFields": [ "collocation_points_sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "MlPceExpansionOrderSequenceCollocRatioLars": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "least_angle_regression": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "required": [ "least_angle_regression" ], "title": "MlPceExpansionOrderSequenceCollocRatioLars", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioLarsConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLasso": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "required": [ "least_absolute_shrinkage" ], "title": "MlPceExpansionOrderSequenceCollocRatioLasso", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioLassoConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquares": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using least squares", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon" }, { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquares", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioOMP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioOMPConfig", "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": "MlPceExpansionOrderSequenceCollocRatioOMP", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioOMPConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioOMPConfig", "type": "object" } }, "additionalProperties": false, "required": [ "collocation_ratio" ] }
- field collocation_ratio: MlPceExpansionOrderSequenceCollocRatioConfig [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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioBP
Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioBP", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioBPDN
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": "MlPceExpansionOrderSequenceCollocRatioBPDN", "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": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "$defs": { "MlPceExpansionOrderSequenceCollocRatioBPDNConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "type": "object" } }, "additionalProperties": false, "required": [ "basis_pursuit_denoising" ] }
- field basis_pursuit_denoising: MlPceExpansionOrderSequenceCollocRatioBPDNConfig [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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioBPDNConfig
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": "MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioCV
Use cross validation to choose the ‘best’ polynomial order of a polynomial chaos expansion.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioCV", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioConfig
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
{ "title": "MlPceExpansionOrderSequenceCollocRatioConfig", "description": "Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.", "type": "object", "properties": { "sequence": { "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": "Sequence", "type": "number", "x-materialization": [ { "ir_key": "method.nond.collocation_ratio", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "collocation_points_sequence": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Sequence of collocation point counts used in a multi-stage expansion", "title": "Collocation Points Sequence", "x-aliases": [ "pilot_samples" ], "x-materialization": [ { "ir_key": "method.nond.collocation_points_sequence", "ir_value_type": "SizetArray", "storage_type": "DIRECT_VALUE" } ] }, "regression_method": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquares" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioOMP" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBP" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBPDN" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLars" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLasso" }, { "type": "null" } ], "default": null, "description": "Regression Algorithm", "title": "Regression Method", "x-union-pattern": 2 }, "cross_validation": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioCV" }, { "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" } ] } }, "$defs": { "MlPceExpansionOrderSequenceCollocRatioBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioBP", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "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": "MlPceExpansionOrderSequenceCollocRatioBPDN", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBPDNConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioCV": { "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": "MlPceExpansionOrderSequenceCollocRatioCV", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLars": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "least_angle_regression": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "required": [ "least_angle_regression" ], "title": "MlPceExpansionOrderSequenceCollocRatioLars", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLarsConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioLarsConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLasso": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLassoConfig", "x-aliases": [ "lasso" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LASSO_REGRESSION" } ] } }, "required": [ "least_absolute_shrinkage" ], "title": "MlPceExpansionOrderSequenceCollocRatioLasso", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLassoConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "noise_tolerance": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "The noise tolerance used when performing cross validation in the presence of noise or truncation errors.", "title": "Noise Tolerance", "x-materialization": [ { "ir_key": "method.nond.regression_noise_tolerance", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] }, "l2_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "description": "The :math:`l_2` pentalty used when performing compressed sensing with elastic net.", "title": "L2 Penalty", "x-materialization": [ { "ir_key": "method.nond.regression_penalty", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioLassoConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquares": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using least squares", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon" }, { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquares", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioOMP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioOMPConfig", "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": "MlPceExpansionOrderSequenceCollocRatioOMP", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioOMPConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioOMPConfig", "type": "object" } }, "additionalProperties": false, "required": [ "sequence" ], "x-model-validations": [ { "validationContext": "mlpceexpansionordersequencecollocratioconfig", "validationErrorMessage": "For mlpceexpansionordersequencecollocratioconfig, all elements of collocation_points_sequence must be >= 0.", "validationFields": [ "collocation_points_sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }
- Fields:
- field collocation_points_sequence: list[int] | None = None
Sequence of collocation point counts used in a multi-stage expansion
- field cross_validation: MlPceExpansionOrderSequenceCollocRatioCV | 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: MlPceExpansionOrderSequenceCollocRatioLeastSquares | MlPceExpansionOrderSequenceCollocRatioOMP | MlPceExpansionOrderSequenceCollocRatioBP | MlPceExpansionOrderSequenceCollocRatioBPDN | MlPceExpansionOrderSequenceCollocRatioLars | MlPceExpansionOrderSequenceCollocRatioLasso | 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 sequence: 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
- 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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioLars
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioLars", "description": "Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.", "type": "object", "properties": { "least_angle_regression": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLarsConfig", "x-aliases": [ "lars" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEAST_ANGLE_REGRESSION" } ] } }, "$defs": { "MlPceExpansionOrderSequenceCollocRatioLarsConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioLarsConfig", "type": "object" } }, "additionalProperties": false, "required": [ "least_angle_regression" ] }
- field least_angle_regression: MlPceExpansionOrderSequenceCollocRatioLarsConfig [Required]
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioLarsConfig
Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioLarsConfig", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioLasso
Compute the coefficients of a polynomial expansion by using the LASSO problem.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioLasso", "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "type": "object", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLassoConfig", "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": { "MlPceExpansionOrderSequenceCollocRatioLassoConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioLassoConfig", "type": "object" } }, "additionalProperties": false, "required": [ "least_absolute_shrinkage" ] }
- field least_absolute_shrinkage: MlPceExpansionOrderSequenceCollocRatioLassoConfig [Required]
Compute the coefficients of a polynomial expansion by using the LASSO problem.
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioLassoConfig
Compute the coefficients of a polynomial expansion by using the LASSO problem.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioLassoConfig", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioLeastSquares
Compute the coefficients of a polynomial expansion using least squares
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioLeastSquares", "description": "Compute the coefficients of a polynomial expansion using least squares", "type": "object", "properties": { "least_squares": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon" }, { "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": { "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd", "type": "object" } }, "additionalProperties": false, "required": [ "least_squares" ] }
- field least_squares: MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd | MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon | dict [Required]
Compute the coefficients of a polynomial expansion using least squares
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon
Calculate the coefficients of a polynomial chaos expansion using equality constrained least squares.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd
Calculate the coefficients of a polynomial chaos expansion using the singular value decomposition.
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioOMP
Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioOMP", "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "type": "object", "properties": { "orthogonal_matching_pursuit": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioOMPConfig", "x-aliases": [ "omp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_MATCH_PURSUIT" } ] } }, "$defs": { "MlPceExpansionOrderSequenceCollocRatioOMPConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioOMPConfig", "type": "object" } }, "additionalProperties": false, "required": [ "orthogonal_matching_pursuit" ] }
- field orthogonal_matching_pursuit: MlPceExpansionOrderSequenceCollocRatioOMPConfig [Required]
Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceCollocRatioOMPConfig
Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceCollocRatioOMPConfig", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceConfig
Sequence of expansion orders used in a multi-stage expansion
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceConfig", "description": "Sequence of expansion orders used in a multi-stage expansion", "type": "object", "properties": { "sequence": { "description": "Sequence of expansion orders used in a multi-stage expansion", "items": { "type": "integer" }, "title": "Sequence", "type": "array", "x-materialization": [ { "ir_key": "method.nond.expansion_order_sequence", "ir_value_type": "UShortArray", "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/MlPceExpansionOrderSequenceBasisTypeTensorProduct" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceBasisTypeTotalOrder" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceBasisTypeAdapted" }, { "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_sequence_selection": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatio" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceExpansionSamplesSequence" } ], "title": "Point Sequence Selection", "x-union-pattern": 4 }, "import_build_points_file": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] } }, "$defs": { "MlPceExpansionOrderSequenceBasisTypeAdapted": { "additionalProperties": false, "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "properties": { "adapted": { "$ref": "#/$defs/MlPceExpansionOrderSequenceBasisTypeAdaptedConfig", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_basis_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ADAPTED_BASIS_EXPANDING_FRONT" } ] } }, "required": [ "adapted" ], "title": "MlPceExpansionOrderSequenceBasisTypeAdapted", "type": "object" }, "MlPceExpansionOrderSequenceBasisTypeAdaptedConfig": { "additionalProperties": false, "description": "Use adaptive basis selection to choose the basis terms in a polynomial chaos expansion.", "properties": { "advancements": { "default": 3, "description": "The maximum number of steps used to expand a basis step.", "title": "Advancements", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.adapted_basis.advancements", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, 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"properties": { "collocation_ratio": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioConfig", "argument": "sequence" } }, "required": [ "collocation_ratio" ], "title": "MlPceExpansionOrderSequenceCollocRatio", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "properties": { "basis_pursuit": { "const": true, "default": true, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit L1 -minimization problem using linear programming.", "title": "Basis Pursuit", "type": "boolean", "x-aliases": [ "bp" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT" } ] } }, "title": "MlPceExpansionOrderSequenceCollocRatioBP", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBPDN": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { "basis_pursuit_denoising": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBPDNConfig", "x-aliases": [ "bpdn" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "BASIS_PURSUIT_DENOISING" } ] } }, "required": [ "basis_pursuit_denoising" ], "title": "MlPceExpansionOrderSequenceCollocRatioBPDN", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioBPDNConfig": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising L1 -minimization problem using second order cone optimization.", "properties": { 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"x-materialization": [ { "ir_key": "method.nond.collocation_ratio", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "collocation_points_sequence": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Sequence of collocation point counts used in a multi-stage expansion", "title": "Collocation Points Sequence", "x-aliases": [ "pilot_samples" ], "x-materialization": [ { "ir_key": "method.nond.collocation_points_sequence", "ir_value_type": "SizetArray", "storage_type": "DIRECT_VALUE" } ] }, "regression_method": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLeastSquares" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioOMP" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBP" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioBPDN" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLars" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLasso" }, { "type": "null" } ], "default": null, "description": "Regression Algorithm", "title": "Regression Method", "x-union-pattern": 2 }, "cross_validation": { "anyOf": [ { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioCV" }, { "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": [ "sequence" ], "title": "MlPceExpansionOrderSequenceCollocRatioConfig", "type": "object", "x-model-validations": [ { "validationContext": 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"MlPceExpansionOrderSequenceCollocRatioLarsConfig": { "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": "MlPceExpansionOrderSequenceCollocRatioLarsConfig", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLasso": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion by using the LASSO problem.", "properties": { "least_absolute_shrinkage": { "$ref": "#/$defs/MlPceExpansionOrderSequenceCollocRatioLassoConfig", "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": "MlPceExpansionOrderSequenceCollocRatioLasso", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLassoConfig": { "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": 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"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": "MlPceExpansionOrderSequenceCollocRatioLeastSquares", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresEqCon", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd": { "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": "MlPceExpansionOrderSequenceCollocRatioLeastSquaresSvd", "type": "object" }, "MlPceExpansionOrderSequenceCollocRatioOMP": { "additionalProperties": false, "description": "Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP)", "properties": { "orthogonal_matching_pursuit": { "$ref": 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A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "properties": { "expansion_samples_sequence": { "$ref": "#/$defs/MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig", "argument": "sequence" } }, "required": [ "expansion_samples_sequence" ], "title": "MlPceExpansionOrderSequenceExpansionSamplesSequence", "type": "object" }, "MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig": { "additionalProperties": false, "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "properties": { "sequence": { "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. 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"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": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "MlPceExpansionOrderSequenceImportBuildPointsFileFreeform": { "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": "MlPceExpansionOrderSequenceImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "sequence", "point_sequence_selection" ], "x-model-validations": [ { "validationContext": "mlpceexpansionordersequenceconfig", "validationErrorMessage": "For mlpceexpansionordersequenceconfig, all elements of sequence must be >= 0.", "validationFields": [ "sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }
- Fields:
- field basis_type: MlPceExpansionOrderSequenceBasisTypeTensorProduct | MlPceExpansionOrderSequenceBasisTypeTotalOrder | MlPceExpansionOrderSequenceBasisTypeAdapted | 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: MlPceExpansionOrderSequenceImportBuildPointsFile | None = None
File containing points you wish to use to build a surrogate
- field point_sequence_selection: MlPceExpansionOrderSequenceCollocRatio | MlPceExpansionOrderSequenceExpansionSamplesSequence [Required]
- field sequence: list[int] [Required]
Sequence of expansion orders used in a multi-stage expansion
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceExpansionSamplesSequence
Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the
expansion_samples_sequenceapplies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g.,expansion_samplesShow JSON schema
{ "title": "MlPceExpansionOrderSequenceExpansionSamplesSequence", "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "type": "object", "properties": { "expansion_samples_sequence": { "$ref": "#/$defs/MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig", "argument": "sequence" } }, "$defs": { "MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig": { "additionalProperties": false, "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "properties": { "sequence": { "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "items": { "type": "integer" }, "title": "Sequence", "type": "array", "x-materialization": [ { "ir_key": "method.nond.expansion_samples_sequence", "ir_value_type": "SizetArray", "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" } ] } }, "required": [ "sequence" ], "title": "MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig", "type": "object", "x-model-validations": [ { "validationContext": "mlpceexpansionordersequenceexpansionsamplessequenceconfig", "validationErrorMessage": "For mlpceexpansionordersequenceexpansionsamplessequenceconfig, all elements of sequence must be >= 0.", "validationFields": [ "sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] } }, "additionalProperties": false, "required": [ "expansion_samples_sequence" ] }
- field expansion_samples_sequence: MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig [Required]
Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the
expansion_samples_sequenceapplies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g.,expansion_samples
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig
Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the
expansion_samples_sequenceapplies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g.,expansion_samplesShow JSON schema
{ "title": "MlPceExpansionOrderSequenceExpansionSamplesSequenceConfig", "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "type": "object", "properties": { "sequence": { "description": "Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the ``expansion_samples_sequence`` applies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g., :dakkw:`method-polynomial_chaos-expansion_order-expansion_samples`", "items": { "type": "integer" }, "title": "Sequence", "type": "array", "x-materialization": [ { "ir_key": "method.nond.expansion_samples_sequence", "ir_value_type": "SizetArray", "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, "required": [ "sequence" ], "x-model-validations": [ { "validationContext": "mlpceexpansionordersequenceexpansionsamplessequenceconfig", "validationErrorMessage": "For mlpceexpansionordersequenceexpansionsamplessequenceconfig, all elements of sequence must be >= 0.", "validationFields": [ "sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }
- field reuse_points: Literal[True] | None = None
This describes the behavior of reuse of points in constructing polynomial chaos expansion models.
- field sequence: list[int] [Required]
Sequence of expansion samples used in a multi-stage polynomial chaos expansion Each level entry of the
expansion_samples_sequenceapplies to one expansion within a multi-stage expansion. Current multi-stage expansions that support expansion samples sequences include multilevel and multifidelity polynomial chaos. If adaptive refinement is active, then this sequence specifies the starting point for each level within either an individual or integrated refinement approach. A corresponding scalar specification is documented at, e.g.,expansion_samples
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceImportBuildPointsFile
File containing points you wish to use to build a surrogate
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceImportBuildPointsFile", "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": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "$defs": { "MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated": { "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": "MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated", "type": "object" }, "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotated", "type": "object" }, "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig": { "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": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "MlPceExpansionOrderSequenceImportBuildPointsFileFreeform": { "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": "MlPceExpansionOrderSequenceImportBuildPointsFileFreeform", "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: MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotated | MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated | MlPceExpansionOrderSequenceImportBuildPointsFileFreeform [Optional]
Tabular Format
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated
Selects annotated tabular file format
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceImportBuildPointsFileAnnotated", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotated
Selects custom-annotated tabular file format
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotated", "description": "Selects custom-annotated tabular file format", "type": "object", "properties": { "custom_annotated": { "$ref": "#/$defs/MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig" } }, "$defs": { "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig": { "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": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig", "type": "object" } }, "additionalProperties": false }
- field custom_annotated: MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig [Optional]
Selects custom-annotated tabular file format
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig
Selects custom-annotated tabular file format
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceImportBuildPointsFileCustomAnnotatedConfig", "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.multilevel_polynomial_chaos.MlPceExpansionOrderSequenceImportBuildPointsFileFreeform
Selects freeform file format
Show JSON schema
{ "title": "MlPceExpansionOrderSequenceImportBuildPointsFileFreeform", "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.multilevel_polynomial_chaos.MlPceOrthogLeastInterp
Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.
Show JSON schema
{ "title": "MlPceOrthogLeastInterp", "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "type": "object", "properties": { "orthogonal_least_interpolation": { "$ref": "#/$defs/MlPceOrthogLeastInterpConfig", "x-aliases": [ "least_interpolation", "oli" ], "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ORTHOG_LEAST_INTERPOLATION" } ] } }, "$defs": { "MlPceOrthogLeastInterpConfig": { "additionalProperties": false, "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "properties": { "collocation_points_sequence": { "description": "Sequence of collocation point counts used in a multi-stage expansion", "items": { "type": "integer" }, "title": "Collocation Points Sequence", "type": "array", "x-materialization": [ { "ir_key": "method.nond.collocation_points_sequence", "ir_value_type": "SizetArray", "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/MlPceOrthogLeastInterpImportBuildPointsFile" }, { "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_sequence" ], "title": "MlPceOrthogLeastInterpConfig", "type": "object", "x-model-validations": [ { "validationContext": "mlpceorthogleastinterpconfig", "validationErrorMessage": "For mlpceorthogleastinterpconfig, all elements of collocation_points_sequence must be >= 0.", "validationFields": [ "collocation_points_sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" }, { "validationContext": "mlpceorthogleastinterpconfig", "validationErrorMessage": "For mlpceorthogleastinterpconfig, all elements of tensor_grid must be >= 0.", "validationFields": [ "tensor_grid" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "MlPceOrthogLeastInterpImportBuildPointsFile": { "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/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated", "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": "MlPceOrthogLeastInterpImportBuildPointsFile", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileFreeform": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "orthogonal_least_interpolation" ] }
- field orthogonal_least_interpolation: MlPceOrthogLeastInterpConfig [Required]
Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceOrthogLeastInterpConfig
Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.
Show JSON schema
{ "title": "MlPceOrthogLeastInterpConfig", "description": "Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation.", "type": "object", "properties": { "collocation_points_sequence": { "description": "Sequence of collocation point counts used in a multi-stage expansion", "items": { "type": "integer" }, "title": "Collocation Points Sequence", "type": "array", "x-materialization": [ { "ir_key": "method.nond.collocation_points_sequence", "ir_value_type": "SizetArray", "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/MlPceOrthogLeastInterpImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] } }, "$defs": { "MlPceOrthogLeastInterpImportBuildPointsFile": { "additionalProperties": false, "description": "File containing points you wish to use to build a surrogate", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "MlPceOrthogLeastInterpImportBuildPointsFile", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileFreeform": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "collocation_points_sequence" ], "x-model-validations": [ { "validationContext": "mlpceorthogleastinterpconfig", "validationErrorMessage": "For mlpceorthogleastinterpconfig, all elements of collocation_points_sequence must be >= 0.", "validationFields": [ "collocation_points_sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" }, { "validationContext": "mlpceorthogleastinterpconfig", "validationErrorMessage": "For mlpceorthogleastinterpconfig, all elements of tensor_grid must be >= 0.", "validationFields": [ "tensor_grid" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }
- Fields:
- field collocation_points_sequence: list[int] [Required]
Sequence of collocation point counts used in a multi-stage expansion
- field import_build_points_file: MlPceOrthogLeastInterpImportBuildPointsFile | 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.multilevel_polynomial_chaos.MlPceOrthogLeastInterpImportBuildPointsFile
File containing points you wish to use to build a surrogate
Show JSON schema
{ "title": "MlPceOrthogLeastInterpImportBuildPointsFile", "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": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "$defs": { "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "MlPceOrthogLeastInterpImportBuildPointsFileFreeform": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileFreeform", "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: MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated | MlPceOrthogLeastInterpImportBuildPointsFileAnnotated | MlPceOrthogLeastInterpImportBuildPointsFileFreeform [Optional]
Tabular Format
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceOrthogLeastInterpImportBuildPointsFileAnnotated
Selects annotated tabular file format
Show JSON schema
{ "title": "MlPceOrthogLeastInterpImportBuildPointsFileAnnotated", "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.multilevel_polynomial_chaos.MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated
Selects custom-annotated tabular file format
Show JSON schema
{ "title": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotated", "description": "Selects custom-annotated tabular file format", "type": "object", "properties": { "custom_annotated": { "$ref": "#/$defs/MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig" } }, "$defs": { "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig": { "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": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "type": "object" } }, "additionalProperties": false }
- field custom_annotated: MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig [Optional]
Selects custom-annotated tabular file format
- pydantic model dakota.spec.method.multilevel_polynomial_chaos.MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig
Selects custom-annotated tabular file format
Show JSON schema
{ "title": "MlPceOrthogLeastInterpImportBuildPointsFileCustomAnnotatedConfig", "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.multilevel_polynomial_chaos.MlPceOrthogLeastInterpImportBuildPointsFileFreeform
Selects freeform file format
Show JSON schema
{ "title": "MlPceOrthogLeastInterpImportBuildPointsFileFreeform", "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
