function_train
- pydantic model dakota.spec.method.function_train.FtSelection
Generated model for FtSelection
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{ "title": "FtSelection", "description": "Generated model for FtSelection", "type": "object", "properties": { "function_train": { "$ref": "#/$defs/FtConfig", "x-materialization": [ { "ir_key": "method.algorithm", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "C3_FUNCTION_TRAIN" } ] } }, "$defs": { "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "ExpansionOptionsDiagCov": { "additionalProperties": false, "description": "Display only the diagonal terms of the covariance matrix", "properties": { "diagonal_covariance": { "const": true, "default": true, "description": "Display only the diagonal terms of the covariance matrix", "title": "Diagonal Covariance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.covariance_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIAGONAL_COVARIANCE" } ] } }, "title": "ExpansionOptionsDiagCov", "type": "object" }, "ExpansionOptionsDistributionComplementary": { "additionalProperties": false, "description": "Computes statistics according to complementary cumulative functions", "properties": { "complementary": { "const": true, "default": true, "description": "Computes statistics according to complementary cumulative functions", "title": "Complementary", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMPLEMENTARY" } ] } }, "title": "ExpansionOptionsDistributionComplementary", "type": "object" }, "ExpansionOptionsDistributionCumulative": { "additionalProperties": false, "description": "Computes statistics according to cumulative functions", "properties": { "cumulative": { "const": true, "default": true, "description": "Computes statistics according to cumulative functions", "title": "Cumulative", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CUMULATIVE" } ] } }, "title": "ExpansionOptionsDistributionCumulative", "type": "object" }, "ExpansionOptionsExportApproxPointsFile": { "additionalProperties": false, "description": "Output file for surrogate model value evaluations", "properties": { "filename": { "description": "Output file for surrogate model value evaluations", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.export_approx_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileCustomAnnotated" }, { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileAnnotated" }, { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "ExpansionOptionsExportApproxPointsFileAnnotated", "x-union-pattern": 1 } }, "required": [ "filename" ], "title": "ExpansionOptionsExportApproxPointsFile", "type": "object" }, "ExpansionOptionsExportApproxPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileAnnotated", "type": "object" }, "ExpansionOptionsExportApproxPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig" } }, "title": "ExpansionOptionsExportApproxPointsFileCustomAnnotated", "type": "object" }, "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig", "type": "object" }, "ExpansionOptionsExportApproxPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileFreeform", "type": "object" }, "ExpansionOptionsFinalMomentsCentral": { "additionalProperties": false, "description": "Output central moments and include them within the set of final statistics.", "properties": { "central": { "const": true, "default": true, "description": "Output central moments and include them within the set of final statistics.", "title": "Central", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CENTRAL_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsCentral", "type": "object" }, "ExpansionOptionsFinalMomentsNoneKeyword": { "additionalProperties": false, "description": "Omit moments from the set of final statistics.", "properties": { "none": { "const": true, "default": true, "description": "Omit moments from the set of final statistics.", "title": "None", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NO_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsNoneKeyword", "type": "object" }, "ExpansionOptionsFinalMomentsStandard": { "additionalProperties": false, "description": "Output standardized moments and include them within the set of final statistics.", "properties": { "standard": { "const": true, "default": true, "description": "Output standardized moments and include them within the set of final statistics.", "title": "Standard", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "STANDARD_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsStandard", "type": "object" }, "ExpansionOptionsFullCov": { "additionalProperties": false, "description": "Display the full covariance matrix", "properties": { "full_covariance": { "const": true, "default": true, "description": "Display the full covariance matrix", "title": "Full Covariance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.covariance_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "FULL_COVARIANCE" } ] } }, "title": "ExpansionOptionsFullCov", "type": "object" }, "ExpansionOptionsGenReliabilityLevels": { "additionalProperties": false, "description": "Specify generalized relability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify generalized relability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_gen_reliability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``gen_reliability_levels`` correspond to which response", "title": "Num Gen Reliability Levels" } }, "required": [ "values" ], "title": "ExpansionOptionsGenReliabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsgenreliabilitylevels", "validationErrorMessage": "For expansionoptionsgenreliabilitylevels, sum of num_gen_reliability_levels must equal length of values.", "validationFields": [ "num_gen_reliability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsProbabilityLevels": { "additionalProperties": false, "description": "Specify probability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify probability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_probability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``probability_levels`` correspond to which response", "title": "Num Probability Levels" } }, "required": [ "values" ], "title": "ExpansionOptionsProbabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsprobabilitylevels", "validationErrorMessage": "For expansionoptionsprobabilitylevels, all elements of values must be in [0, 1].", "validationFields": [ "values" ], "validationLiterals": [], "validationRuleName": "check_probability_list" }, { "validationContext": "expansionoptionsprobabilitylevels", "validationErrorMessage": "For expansionoptionsprobabilitylevels, sum of num_probability_levels must equal length of values.", "validationFields": [ "num_probability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsProbabilityRefinement": { "additionalProperties": false, "description": "Allow refinement of probability and generalized reliability results using importance sampling", "properties": { "importance_sampling_approach": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinementImportance" }, { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinementAdaptImport" }, { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinementMmAdaptImport" } ], "description": "Importance Sampling Approach", "title": "Importance Sampling Approach", "x-union-pattern": 4 }, "refinement_samples": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of samples used to refine a probability estimate or sampling design.", "title": "Refinement Samples", "x-materialization": [ { "ir_key": "method.nond.refinement_samples", "ir_value_type": "IntVector", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "importance_sampling_approach" ], "title": "ExpansionOptionsProbabilityRefinement", "type": "object" }, "ExpansionOptionsProbabilityRefinementAdaptImport": { "additionalProperties": false, "description": "Importance sampling option for probability refinement", "properties": { "adapt_import": { "const": true, "default": true, "description": "Importance sampling option for probability refinement", "title": "Adapt Import", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.integration_refinement", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "AIS" } ] } }, "title": "ExpansionOptionsProbabilityRefinementAdaptImport", "type": "object" }, "ExpansionOptionsProbabilityRefinementImportance": { "additionalProperties": false, "description": "Importance sampling option for probability refinement", "properties": { "importance": { "const": true, "default": true, "description": "Importance sampling option for probability refinement", "title": "Importance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.integration_refinement", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "IS" } ] } }, "title": "ExpansionOptionsProbabilityRefinementImportance", "type": "object" }, "ExpansionOptionsProbabilityRefinementMmAdaptImport": { "additionalProperties": false, "description": "Importance sampling option for probability refinement", "properties": { "mm_adapt_import": { "const": true, "default": true, "description": "Importance sampling option for probability refinement", "title": "Mm Adapt Import", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.integration_refinement", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "MMAIS" } ] } }, "title": "ExpansionOptionsProbabilityRefinementMmAdaptImport", "type": "object" }, "ExpansionOptionsReliabilityLevels": { "additionalProperties": false, "description": "Specify reliability levels at which the response values will be estimated", "properties": { "values": { "description": "Specify reliability levels at which the response values will be estimated", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_reliability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``reliability_levels`` correspond to which response", "title": "Num Reliability Levels" } }, "required": [ "values" ], "title": "ExpansionOptionsReliabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsreliabilitylevels", "validationErrorMessage": "For expansionoptionsreliabilitylevels, sum of num_reliability_levels must equal length of values.", "validationFields": [ "num_reliability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsResponseLevels": { "additionalProperties": false, "description": "Values at which to estimate desired statistics for each response", "properties": { "values": { "description": "Values at which to estimate desired statistics for each response", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_response_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of values at which to estimate desired statistics for each response", "title": "Num Response Levels" }, "compute": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevelsCompute" }, { "type": "null" } ], "default": null, "description": "Selection of statistics to compute at each response level" } }, "required": [ "values" ], "title": "ExpansionOptionsResponseLevels", "type": "object", "x-model-validations": [ { "validationContext": "expansionoptionsresponselevels", "validationErrorMessage": "For expansionoptionsresponselevels, sum of num_response_levels must equal length of values.", "validationFields": [ "num_response_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ExpansionOptionsResponseLevelsCompute": { "additionalProperties": false, "description": "Selection of statistics to compute at each response level", "properties": { "statistic": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeProbabilities" }, { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeReliabilities" }, { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeGenReliabilities" } ], "description": "Statistics to Compute", "title": "Statistic", "x-union-pattern": 4 }, "system": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeSystemSeries" }, { "$ref": "#/$defs/ExpansionOptionsResponseLevelsComputeSystemParallel" }, { "type": "null" } ], "default": null, "description": "Compute system reliability (series or parallel)", "title": "System", "x-union-pattern": 2 } }, "required": [ "statistic" ], "title": "ExpansionOptionsResponseLevelsCompute", "type": "object" }, "ExpansionOptionsResponseLevelsComputeGenReliabilities": { "additionalProperties": false, "description": "Computes generalized reliabilities associated with response levels", "properties": { "gen_reliabilities": { "const": true, "default": true, "description": "Computes generalized reliabilities associated with response levels", "title": "Gen Reliabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "GEN_RELIABILITIES" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeGenReliabilities", "type": "object" }, "ExpansionOptionsResponseLevelsComputeProbabilities": { "additionalProperties": false, "description": "Computes probabilities associated with response levels", "properties": { "probabilities": { "const": true, "default": true, "description": "Computes probabilities associated with response levels", "title": "Probabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "PROBABILITIES" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeProbabilities", "type": "object" }, "ExpansionOptionsResponseLevelsComputeReliabilities": { "additionalProperties": false, "description": "Computes reliabilities associated with response levels", "properties": { "reliabilities": { "const": true, "default": true, "description": "Computes reliabilities associated with response levels", "title": "Reliabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RELIABILITIES" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeReliabilities", "type": "object" }, "ExpansionOptionsResponseLevelsComputeSystemParallel": { "additionalProperties": false, "description": "Aggregate response statistics assuming a parallel system", "properties": { "parallel": { "const": true, "default": true, "description": "Aggregate response statistics assuming a parallel system", "title": "Parallel", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target_reduce", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SYSTEM_PARALLEL" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeSystemParallel", "type": "object" }, "ExpansionOptionsResponseLevelsComputeSystemSeries": { "additionalProperties": false, "description": "Aggregate response statistics assuming a series system", "properties": { "series": { "const": true, "default": true, "description": "Aggregate response statistics assuming a series system", "title": "Series", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target_reduce", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SYSTEM_SERIES" } ] } }, "title": "ExpansionOptionsResponseLevelsComputeSystemSeries", "type": "object" }, "ExpansionOptionsRngMt19937": { "additionalProperties": false, "description": "Generates random numbers using the Mersenne twister", "properties": { "mt19937": { "const": true, "default": true, "description": "Generates random numbers using the Mersenne twister", "title": "Mt19937", "type": "boolean", "x-materialization": [ { "ir_key": "method.random_number_generator", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "mt19937" } ] } }, "title": "ExpansionOptionsRngMt19937", "type": "object" }, "ExpansionOptionsRngRnum2": { "additionalProperties": false, "description": "Generates pseudo-random numbers using the Pecos package", "properties": { "rnum2": { "const": true, "default": true, "description": "Generates pseudo-random numbers using the Pecos package", "title": "Rnum2", "type": "boolean", "x-materialization": [ { "ir_key": "method.random_number_generator", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "rnum2" } ] } }, "title": "ExpansionOptionsRngRnum2", "type": "object" }, "ExpansionOptionsSampleTypeLhs": { "additionalProperties": false, "description": "Uses Latin Hypercube Sampling (LHS) to sample variables", "properties": { "lhs": { "const": true, "default": true, "description": "Uses Latin Hypercube Sampling (LHS) to sample variables", "title": "Lhs", "type": "boolean", "x-materialization": [ { "ir_key": "method.sample_type", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "SUBMETHOD_LHS" } ] } }, "title": "ExpansionOptionsSampleTypeLhs", "type": "object" }, "ExpansionOptionsSampleTypeRandom": { "additionalProperties": false, "description": "Uses purely random Monte Carlo sampling to sample variables", "properties": { "random": { "const": true, "default": true, "description": "Uses purely random Monte Carlo sampling to sample variables", "title": "Random", "type": "boolean", "x-materialization": [ { "ir_key": "method.sample_type", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "SUBMETHOD_RANDOM" } ] } }, "title": "ExpansionOptionsSampleTypeRandom", "type": "object" }, "ExpansionOptionsVarianceBasedDecomp": { "additionalProperties": false, "description": "Activates global sensitivity analysis based on decomposition of response variance into main, interaction, and total effects", "properties": { "interaction_order": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "Specify the maximum number of variables allowed in an interaction when reporting interaction metrics.", "title": "Interaction Order", "x-materialization": [ { "ir_key": "method.nond.vbd_interaction_order", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "drop_tolerance": { "default": -1.0, "description": "Suppresses output of sensitivity indices with values lower than this tolerance", "title": "Drop Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.vbd_drop_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "title": "ExpansionOptionsVarianceBasedDecomp", "type": "object" }, "FtConfig": { "additionalProperties": false, "description": "UQ method leveraging a functional tensor train surrogate model.", "properties": { "model_pointer": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Identifier for model block to be used by a method", "title": "Model Pointer", "x-block-pointer": "model", "x-materialization": [ { "ir_key": "method.model_pointer", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "seed": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "Seed of the random number generator", "title": "Seed", "x-materialization": [ { "ir_key": "method.random_seed", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "fixed_seed": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Reuses the same seed value for multiple random sampling sets", "title": "Fixed Seed", "x-materialization": [ { "ir_key": "method.fixed_seed", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "samples_on_emulator": { "default": 0, "description": "Number of samples at which to evaluate an emulator (surrogate)", "title": "Samples On Emulator", "type": "integer", "x-aliases": [ "samples" ], "x-materialization": [ { "ir_key": "method.nond.samples_on_emulator", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "sample_type": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsSampleTypeLhs" }, { "$ref": "#/$defs/ExpansionOptionsSampleTypeRandom" }, { "type": "null" } ], "default": null, "description": "Selection of sampling strategy", "title": "Sample Type", "x-union-pattern": 2 }, "rng": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsRngMt19937" }, { "$ref": "#/$defs/ExpansionOptionsRngRnum2" } ], "description": "Selection of a random number generator", "title": "Rng", "x-model-default": "ExpansionOptionsRngMt19937", "x-union-pattern": 1 }, "probability_refinement": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinement" }, { "type": "null" } ], "default": null, "description": "Allow refinement of probability and generalized reliability results using importance sampling", "x-aliases": [ "sample_refinement" ] }, "final_moments": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsFinalMomentsNoneKeyword" }, { "$ref": "#/$defs/ExpansionOptionsFinalMomentsStandard" }, { "$ref": "#/$defs/ExpansionOptionsFinalMomentsCentral" } ], "description": "Output moments of the specified type and include them within the set of final statistics.", "title": "Final Moments", "x-model-default": "ExpansionOptionsFinalMomentsStandard", "x-union-pattern": 1 }, "response_levels": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Values at which to estimate desired statistics for each response", "x-materialization": [ { "ir_key": "method.nond.response_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "probability_levels": { "anyOf": [ { "$ref": 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"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": [ "function_train" ] }
- 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.function_train.FtConfig
UQ method leveraging a functional tensor train surrogate model.
Show JSON schema
{ "title": "FtConfig", "description": "UQ method leveraging a functional tensor train surrogate model.", "type": "object", "properties": { "model_pointer": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Identifier for model block to be used by a method", "title": "Model Pointer", "x-block-pointer": "model", "x-materialization": [ { "ir_key": "method.model_pointer", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "seed": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "Seed of the random number generator", "title": "Seed", "x-materialization": [ { "ir_key": "method.random_seed", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "fixed_seed": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Reuses the same seed value for multiple random sampling sets", "title": "Fixed Seed", "x-materialization": [ { "ir_key": "method.fixed_seed", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "samples_on_emulator": { "default": 0, "description": "Number of samples at which to evaluate an emulator (surrogate)", "title": "Samples On Emulator", "type": "integer", "x-aliases": [ "samples" ], "x-materialization": [ { "ir_key": "method.nond.samples_on_emulator", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "sample_type": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsSampleTypeLhs" }, { "$ref": "#/$defs/ExpansionOptionsSampleTypeRandom" }, { "type": "null" } ], "default": null, "description": "Selection of sampling strategy", "title": "Sample Type", "x-union-pattern": 2 }, "rng": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsRngMt19937" }, { "$ref": "#/$defs/ExpansionOptionsRngRnum2" } ], "description": "Selection of a random number generator", "title": "Rng", "x-model-default": "ExpansionOptionsRngMt19937", "x-union-pattern": 1 }, "probability_refinement": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsProbabilityRefinement" }, { "type": "null" } ], "default": null, "description": "Allow refinement of probability and generalized reliability results using importance sampling", "x-aliases": [ "sample_refinement" ] }, "final_moments": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsFinalMomentsNoneKeyword" }, { "$ref": "#/$defs/ExpansionOptionsFinalMomentsStandard" }, { "$ref": "#/$defs/ExpansionOptionsFinalMomentsCentral" } ], "description": "Output moments of the specified type and include them within the set of final statistics.", "title": "Final Moments", "x-model-default": "ExpansionOptionsFinalMomentsStandard", "x-union-pattern": 1 }, "response_levels": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsResponseLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Values at which to estimate desired statistics for each response", "x-materialization": [ { "ir_key": "method.nond.response_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "probability_levels": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsProbabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify probability levels at which to estimate the corresponding response value", "x-materialization": [ { "ir_key": "method.nond.probability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "reliability_levels": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsReliabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify reliability levels at which the response values will be estimated", "x-materialization": [ { "ir_key": "method.nond.reliability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "gen_reliability_levels": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsGenReliabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify generalized relability levels at which to estimate the corresponding response value", "x-materialization": [ { "ir_key": "method.nond.gen_reliability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "distribution": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsDistributionCumulative" }, { "$ref": "#/$defs/ExpansionOptionsDistributionComplementary" } ], "description": "Selection of cumulative or complementary cumulative functions", "title": "Distribution", "x-model-default": "ExpansionOptionsDistributionCumulative", "x-union-pattern": 1 }, "variance_based_decomp": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsVarianceBasedDecomp" }, { "type": "null" } ], "default": null, "description": "Activates global sensitivity analysis based on decomposition of response variance into main, interaction, and total effects", "x-materialization": [ { "ir_key": "method.variance_based_decomp", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "import_approx_points_file": { "anyOf": [ { "$ref": "#/$defs/ImportApproxPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "Filename for points at which to evaluate the PCE/SC surrogate" }, "export_approx_points_file": { "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "Output file for surrogate model value evaluations", "x-aliases": [ "export_points_file" ] }, "covariance_type": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsDiagCov" }, { "$ref": "#/$defs/ExpansionOptionsFullCov" }, { "type": "null" } ], "default": null, "description": "Covariance Type", "title": "Covariance Type", "x-union-pattern": 2 }, "start_rank": { "default": 2, "description": "The initial rank used for the starting point during a rank adaptation.", "minimum": 0, "title": "Start Rank", "type": "integer", "x-aliases": [ "rank" ], "x-materialization": [ { "ir_key": "method.nond.c3function_train.start_rank", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "adapt_rank": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Activate adaptive procedure for determining best rank representation", "title": "Adapt Rank", "x-materialization": [ { "ir_key": "method.nond.c3function_train.adapt_rank", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "kick_rank": { "default": 1, "description": "The increment in rank employed during each iteration of the rank adaptation.", "exclusiveMinimum": 0, "title": "Kick Rank", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.c3function_train.kick_rank", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "max_rank": { "default": 9223372036854775807, "description": "Limits the maximum rank that is explored during a rank adaptation.", "minimum": 0, "title": "Max Rank", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.c3function_train.max_rank", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "max_cv_rank_candidates": { "default": 9223372036854775807, "description": "Limit the number of cross-validation candidates for rank", "minimum": 0, "title": "Max Cv Rank Candidates", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.cross_validation.max_rank_candidates", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "start_order": { "anyOf": [ { "$ref": "#/$defs/FtMethodOrderStartOrder" }, { "type": "null" } ], "argument": "value", "default": null, "description": "(Initial) polynomial order of each univariate function within the functional tensor train.", "x-aliases": [ "order" ] }, "adapt_order": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Activate adaptive procedure for determining the best basis order", "title": "Adapt Order", "x-materialization": [ { "ir_key": "method.nond.c3function_train.adapt_order", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "kick_order": { "default": 1, "description": "increment used when adapting the basis order in function train methods", "exclusiveMinimum": 0, "title": "Kick Order", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.c3function_train.kick_order", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "max_order": { "default": 65535, "description": "Maximum polynomial order of each univariate function within the functional tensor train.", "minimum": 0, "title": "Max Order", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.c3function_train.max_order", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] }, "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" } ] }, "rounding_tolerance": { "default": 1e-10, "description": "An accuracy tolerance that is used to guide rounding during rank adaptation.", "title": "Rounding Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.nond.c3function_train.solver_rounding_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "arithmetic_tolerance": { "default": 1e-10, "description": "A secondary rounding tolerance used for post-processing", "title": "Arithmetic Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.nond.c3function_train.stats_rounding_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "regression_type": { "anyOf": [ { "$ref": "#/$defs/FtMethodRegressionTypeLs" }, { "$ref": "#/$defs/FtMethodRegressionTypeRls2" }, { "type": "null" } ], "default": null, "description": "Type of solver for forming function train approximations by regression", "title": "Regression Type", "x-union-pattern": 2 }, "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" } ] }, "max_cross_iterations": { "default": 1, "description": "Maximum number of iterations for cross-approximation during a rank adaptation.", "minimum": 0, "title": "Max Cross Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.c3function_train.max_cross_iterations", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "solver_tolerance": { "default": 1e-10, "description": "Convergence tolerance for the optimizer used during the regression solve.", "title": "Solver Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.nond.c3function_train.solver_tolerance", "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" } ] }, "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" } ] }, "collocation_control": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/FtMethodRegressionCollocPoints" }, { "$ref": "#/$defs/FtMethodRegressionCollocRatio" } ], "description": "Collocation Control", "title": "Collocation Control", "x-union-pattern": 4 }, "refinement_metric": { "anyOf": [ { "$ref": "#/$defs/LevelMappings" }, { "$ref": "#/$defs/RefinementMetricCov" }, { "type": "null" } ], "default": null, "description": "Metric used for guiding adaptive refinement during UQ.", "title": "Refinement Metric", "x-union-pattern": 2 }, "convergence_tolerance": { "anyOf": [ { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext2ConvergenceTol" }, { "type": "null" } ], "argument": "value", "default": null, "description": "Stopping criterion based on objective function or statistics convergence" }, "p_refinement": { "anyOf": [ { "$ref": "#/$defs/FtMethodRefinementPRefinement" }, { "type": "null" } ], "default": null, "description": "Automatic polynomial order refinement", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "P_REFINEMENT" } ] }, "max_refinement_iterations": { "default": 9223372036854775807, "description": "Maximum number of expansion refinement iterations", "minimum": 0, "title": "Max Refinement Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.max_refinement_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "id_method": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Name the method block; helpful when there are multiple", "title": "Id Method", "x-materialization": [ { "ir_key": "method.id", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "output": { "anyOf": [ { "$ref": "#/$defs/Debug" }, { "$ref": "#/$defs/Verbose" }, { "$ref": "#/$defs/Normal" }, { "$ref": "#/$defs/Quiet" }, { "$ref": "#/$defs/Silent" } ], "description": "Control how much method information is written to the screen and output file", "title": "Output", "x-model-default": "Normal", "x-union-pattern": 1 }, "final_solutions": { "default": 0, "description": "Number of designs returned as the best solutions", "minimum": 0, "title": "Final Solutions", "type": "integer", "x-materialization": [ { "ir_key": "method.final_solutions", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] } }, "$defs": { "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "ExpansionOptionsDiagCov": { "additionalProperties": false, "description": "Display only the diagonal terms of the covariance matrix", "properties": { "diagonal_covariance": { "const": true, "default": true, "description": "Display only the diagonal terms of the covariance matrix", "title": "Diagonal Covariance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.covariance_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DIAGONAL_COVARIANCE" } ] } }, "title": "ExpansionOptionsDiagCov", "type": "object" }, "ExpansionOptionsDistributionComplementary": { "additionalProperties": false, "description": "Computes statistics according to complementary cumulative functions", "properties": { "complementary": { "const": true, "default": true, "description": "Computes statistics according to complementary cumulative functions", "title": "Complementary", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMPLEMENTARY" } ] } }, "title": "ExpansionOptionsDistributionComplementary", "type": "object" }, "ExpansionOptionsDistributionCumulative": { "additionalProperties": false, "description": "Computes statistics according to cumulative functions", "properties": { "cumulative": { "const": true, "default": true, "description": "Computes statistics according to cumulative functions", "title": "Cumulative", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CUMULATIVE" } ] } }, "title": "ExpansionOptionsDistributionCumulative", "type": "object" }, "ExpansionOptionsExportApproxPointsFile": { "additionalProperties": false, "description": "Output file for surrogate model value evaluations", "properties": { "filename": { "description": "Output file for surrogate model value evaluations", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.export_approx_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileCustomAnnotated" }, { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileAnnotated" }, { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "ExpansionOptionsExportApproxPointsFileAnnotated", "x-union-pattern": 1 } }, "required": [ "filename" ], "title": "ExpansionOptionsExportApproxPointsFile", "type": "object" }, "ExpansionOptionsExportApproxPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileAnnotated", "type": "object" }, "ExpansionOptionsExportApproxPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig" } }, "title": "ExpansionOptionsExportApproxPointsFileCustomAnnotated", "type": "object" }, "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileCustomAnnotatedConfig", "type": "object" }, "ExpansionOptionsExportApproxPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "ExpansionOptionsExportApproxPointsFileFreeform", "type": "object" }, "ExpansionOptionsFinalMomentsCentral": { "additionalProperties": false, "description": "Output central moments and include them within the set of final statistics.", "properties": { "central": { "const": true, "default": true, "description": "Output central moments and include them within the set of final statistics.", "title": "Central", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CENTRAL_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsCentral", "type": "object" }, "ExpansionOptionsFinalMomentsNoneKeyword": { "additionalProperties": false, "description": "Omit moments from the set of final statistics.", "properties": { "none": { "const": true, "default": true, "description": "Omit moments from the set of final statistics.", "title": "None", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NO_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsNoneKeyword", "type": "object" }, "ExpansionOptionsFinalMomentsStandard": { "additionalProperties": false, "description": "Output standardized moments and include them within the set of final statistics.", "properties": { "standard": { "const": true, "default": true, "description": "Output standardized moments and include them within the set of final statistics.", "title": "Standard", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.final_moments", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "STANDARD_MOMENTS" } ] } }, "title": "ExpansionOptionsFinalMomentsStandard", "type": "object" }, "ExpansionOptionsFullCov": { "additionalProperties": false, "description": "Display the full covariance matrix", "properties": { "full_covariance": { "const": true, "default": true, "description": "Display the full covariance matrix", "title": "Full Covariance", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.covariance_control", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "FULL_COVARIANCE" } ] } }, "title": "ExpansionOptionsFullCov", "type": "object" }, 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"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": 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"Use least squares solver for forming function train approximations by regression", "properties": { "ls": { "const": true, "default": true, "description": "Use least squares solver for forming function train approximations by regression", "title": "Ls", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "FT_LS" } ] } }, "title": "FtMethodRegressionTypeLs", "type": "object" }, "FtMethodRegressionTypeRls2": { "additionalProperties": false, "description": "Use regularized regression solver for forming function train approximations", "properties": { "rls2": { "$ref": "#/$defs/FtMethodRegressionTypeRls2Config", "x-materialization": [ { "ir_key": "method.nond.regression_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "FT_RLS2" } ] } }, "required": [ "rls2" ], "title": "FtMethodRegressionTypeRls2", "type": "object" }, 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"START_ORDER_ADVANCEMENT" } ] } }, "title": "IncrementStartOrder", "type": "object" }, "IncrementStartRank": { "additionalProperties": false, "description": "candidate generation by advancement of starting rank", "properties": { "increment_start_rank": { "const": true, "default": true, "description": "candidate generation by advancement of starting rank", "title": "Increment Start Rank", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.c3function_train.advancement_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "START_RANK_ADVANCEMENT" } ] } }, "title": "IncrementStartRank", "type": "object" }, "LevelMappings": { "additionalProperties": false, "description": "Utilize the level mappings metric for guiding adaptive refinement during UQ.", "properties": { "level_mappings": { "const": true, "default": true, "description": "Utilize the level mappings metric for guiding adaptive refinement during UQ.", "title": "Level Mappings", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_metric", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEVEL_STATS_METRIC" } ] } }, "title": "LevelMappings", "type": "object" }, "MethodConvergenceTolWithTypeContext2Absolute": { "additionalProperties": false, "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods", "properties": { "absolute": { "const": true, "default": true, "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods", "title": "Absolute", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.convergence_tolerance_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ABSOLUTE_CONVERGENCE_TOLERANCE" } ] } }, "title": "MethodConvergenceTolWithTypeContext2Absolute", "type": "object" }, "MethodConvergenceTolWithTypeContext2ConvergenceTol": { "additionalProperties": false, "description": "Stopping criterion based on objective function or statistics convergence", "properties": { "value": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on objective function or statistics convergence", "title": "Value", "type": "number", "x-materialization": [ { "ir_key": "method.convergence_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" }, { "ir_key": "method.jega.percent_change", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "convergence_tolerance_type": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext2Relative" }, { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext2Absolute" }, { "type": "null" } ], "default": null, "description": "Convergence tolerance type", "title": "Convergence Tolerance Type", "x-union-pattern": 2 } }, "title": "MethodConvergenceTolWithTypeContext2ConvergenceTol", "type": "object" }, "MethodConvergenceTolWithTypeContext2Relative": { "additionalProperties": false, "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark", "properties": { "relative": { "const": true, "default": true, "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark", "title": "Relative", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.convergence_tolerance_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "RELATIVE_CONVERGENCE_TOLERANCE" } ] } }, "title": "MethodConvergenceTolWithTypeContext2Relative", "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" }, "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": [ "collocation_control" ] }
- Fields:
import_approx_points_file (dakota.spec.shared.expansion.options.ImportApproxPointsFile | None)p_refinement (dakota.spec.shared.expansion.function_train.FtMethodRefinementPRefinement | 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)start_order (dakota.spec.shared.expansion.function_train.FtMethodOrderStartOrder | None)
- field adapt_order: Literal[True] | None = None
Activate adaptive procedure for determining the best basis order
- field adapt_rank: Literal[True] | None = None
Activate adaptive procedure for determining best rank representation
- field arithmetic_tolerance: DakotaFloat = 1e-10
A secondary rounding tolerance used for post-processing
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- field collocation_control: FtMethodRegressionCollocPoints | FtMethodRegressionCollocRatio [Required]
Collocation Control
- field convergence_tolerance: MethodConvergenceTolWithTypeContext2ConvergenceTol | None = None
Stopping criterion based on objective function or statistics convergence
- field covariance_type: ExpansionOptionsDiagCov | ExpansionOptionsFullCov | None = None
Covariance Type
- field distribution: ExpansionOptionsDistributionCumulative | ExpansionOptionsDistributionComplementary [Optional]
Selection of cumulative or complementary cumulative functions
- field export_approx_points_file: ExpansionOptionsExportApproxPointsFile | None = None
Output file for surrogate model value evaluations
- field 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 kick_order: int = 1
increment used when adapting the basis order in function train methods
- Constraints:
gt = 0
- field kick_rank: int = 1
The increment in rank employed during each iteration of the rank adaptation.
- Constraints:
gt = 0
- field max_cross_iterations: int = 1
Maximum number of iterations for cross-approximation during a rank adaptation.
- Constraints:
ge = 0
- field max_cv_order_candidates: int = 65535
Limit the number of cross-validation candidates for basis order
- Constraints:
ge = 0
- field max_cv_rank_candidates: int = 9223372036854775807
Limit the number of cross-validation candidates for rank
- Constraints:
ge = 0
- field max_order: int = 65535
Maximum polynomial order of each univariate function within the functional tensor train.
- Constraints:
ge = 0
- field max_rank: int = 9223372036854775807
Limits the maximum rank that is explored during a rank adaptation.
- Constraints:
ge = 0
- field max_refinement_iterations: int = 9223372036854775807
Maximum number of expansion refinement iterations
- Constraints:
ge = 0
- field max_solver_iterations: int = 9223372036854775807
Maximum iterations in determining polynomial coefficients
- Constraints:
ge = 0
- field model_pointer: str | None = None
Identifier for model block to be used by a method
- field output: Debug | Verbose | Normal | Quiet | Silent [Optional]
Control how much method information is written to the screen and output file
- field p_refinement: FtMethodRefinementPRefinement | None = None
Automatic polynomial order refinement
- field probability_levels: ExpansionOptionsProbabilityLevels | None = None
Specify probability levels at which to estimate the corresponding response value
- field probability_refinement: ExpansionOptionsProbabilityRefinement | None = None
Allow refinement of probability and generalized reliability results using importance sampling
- field refinement_metric: LevelMappings | RefinementMetricCov | None = None
Metric used for guiding adaptive refinement during UQ.
- field regression_type: FtMethodRegressionTypeLs | FtMethodRegressionTypeRls2 | None = None
Type of solver for forming function train approximations by regression
- 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 response_scaling: Literal[True] | None = None
Perform bounds-scaling on response values prior to surrogate emulation
- field rng: ExpansionOptionsRngMt19937 | ExpansionOptionsRngRnum2 [Optional]
Selection of a random number generator
- field rounding_tolerance: DakotaFloat = 1e-10
An accuracy tolerance that is used to guide rounding during rank adaptation.
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- field sample_type: ExpansionOptionsSampleTypeLhs | ExpansionOptionsSampleTypeRandom | None = None
Selection of sampling strategy
- field samples_on_emulator: int = 0
Number of samples at which to evaluate an emulator (surrogate)
- field seed: int | None = None
Seed of the random number generator
- Constraints:
gt = 0
- field solver_tolerance: DakotaFloat = 1e-10
Convergence tolerance for the optimizer used during the regression solve.
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- field start_order: FtMethodOrderStartOrder | None = None
(Initial) polynomial order of each univariate function within the functional tensor train.
- field start_rank: int = 2
The initial rank used for the starting point during a rank adaptation.
- Constraints:
ge = 0
- field tensor_grid: Literal[True] | None = None
Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion.
- 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.function_train
