Core
Generated Pydantic models for shared.core
- pydantic model dakota.spec.shared.core.Active
use statistics for the active expansion within multifidelity refinement
Show JSON schema
{ "title": "Active", "description": "use statistics for the active expansion within multifidelity refinement", "type": "object", "properties": { "active": { "const": true, "default": true, "description": "use statistics for the active expansion within multifidelity refinement", "title": "Active", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.refinement_statistics_mode", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ACTIVE_EXPANSION_STATS" } ] } }, "additionalProperties": false }
- Fields:
- field active: Literal[True] = True
use statistics for the active expansion within multifidelity refinement
- pydantic model dakota.spec.shared.core.DefaultConstraintTolMixin
Generated model for DefaultConstraintTolMixin
Show JSON schema
{ "title": "DefaultConstraintTolMixin", "description": "Generated model for DefaultConstraintTolMixin", "type": "object", "properties": { "constraint_tolerance": { "default": 0.0, "description": "Maximum allowable constraint violation still considered feasible", "title": "Constraint Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.constraint_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "additionalProperties": false }
- Fields:
- field constraint_tolerance: DakotaFloat = 0.0
Maximum allowable constraint violation still considered feasible
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- pydantic model dakota.spec.shared.core.LevelMappings
Utilize the level mappings metric for guiding adaptive refinement during UQ.
Show JSON schema
{ "title": "LevelMappings", "description": "Utilize the level mappings metric for guiding adaptive refinement during UQ.", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field level_mappings: Literal[True] = True
Utilize the level mappings metric for guiding adaptive refinement during UQ.
- pydantic model dakota.spec.shared.core.MethodConvergenceTolMixin
Generated model for MethodConvergenceTolMixin
Show JSON schema
{ "title": "MethodConvergenceTolMixin", "description": "Generated model for MethodConvergenceTolMixin", "type": "object", "properties": { "convergence_tolerance": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on objective function or statistics convergence", "title": "Convergence Tolerance", "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" } ] } }, "additionalProperties": false }
- Fields:
- field convergence_tolerance: DakotaFloat = -1.7976931348623157e+308
Stopping criterion based on objective function or statistics convergence
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext1Absolute
Use absolute statistical metrics for assessing convergence in adaptive UQ methods
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext1Absolute", "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field absolute: Literal[True] = True
Use absolute statistical metrics for assessing convergence in adaptive UQ methods
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext1ConvergenceTol
Stopping criterion based on relative error
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext1ConvergenceTol", "description": "Stopping criterion based on relative error", "type": "object", "properties": { "value": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on relative error", "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/MethodConvergenceTolWithTypeContext1Relative" }, { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext1Absolute" }, { "type": "null" } ], "default": null, "description": "Convergence tolerance type", "title": "Convergence Tolerance Type", "x-union-pattern": 2 } }, "$defs": { "MethodConvergenceTolWithTypeContext1Absolute": { "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": "MethodConvergenceTolWithTypeContext1Absolute", "type": "object" }, "MethodConvergenceTolWithTypeContext1Relative": { "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": "MethodConvergenceTolWithTypeContext1Relative", "type": "object" } }, "additionalProperties": false }
- Fields:
- field convergence_tolerance_type: MethodConvergenceTolWithTypeContext1Relative | MethodConvergenceTolWithTypeContext1Absolute | None = None
Convergence tolerance type
- field value: DakotaFloat = -1.7976931348623157e+308
Stopping criterion based on relative error
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext1Mixin
Generated model for MethodConvergenceTolWithTypeContext1Mixin
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext1Mixin", "description": "Generated model for MethodConvergenceTolWithTypeContext1Mixin", "type": "object", "properties": { "convergence_tolerance": { "anyOf": [ { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext1ConvergenceTol" }, { "type": "null" } ], "argument": "value", "default": null, "description": "Stopping criterion based on relative error" } }, "$defs": { "MethodConvergenceTolWithTypeContext1Absolute": { "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": "MethodConvergenceTolWithTypeContext1Absolute", "type": "object" }, "MethodConvergenceTolWithTypeContext1ConvergenceTol": { "additionalProperties": false, "description": "Stopping criterion based on relative error", "properties": { "value": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on relative error", "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/MethodConvergenceTolWithTypeContext1Relative" }, { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext1Absolute" }, { "type": "null" } ], "default": null, "description": "Convergence tolerance type", "title": "Convergence Tolerance Type", "x-union-pattern": 2 } }, "title": "MethodConvergenceTolWithTypeContext1ConvergenceTol", "type": "object" }, "MethodConvergenceTolWithTypeContext1Relative": { "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": "MethodConvergenceTolWithTypeContext1Relative", "type": "object" } }, "additionalProperties": false }
- field convergence_tolerance: MethodConvergenceTolWithTypeContext1ConvergenceTol | None = None
Stopping criterion based on relative error
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext1Relative
Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext1Relative", "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field relative: Literal[True] = True
Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext2Absolute
Use absolute statistical metrics for assessing convergence in adaptive UQ methods
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext2Absolute", "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field absolute: Literal[True] = True
Use absolute statistical metrics for assessing convergence in adaptive UQ methods
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext2ConvergenceTol
Stopping criterion based on objective function or statistics convergence
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext2ConvergenceTol", "description": "Stopping criterion based on objective function or statistics convergence", "type": "object", "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 } }, "$defs": { "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" }, "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" } }, "additionalProperties": false }
- Fields:
- field convergence_tolerance_type: MethodConvergenceTolWithTypeContext2Relative | MethodConvergenceTolWithTypeContext2Absolute | None = None
Convergence tolerance type
- field value: DakotaFloat = -1.7976931348623157e+308
Stopping criterion based on objective function or statistics convergence
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext2Mixin
Generated model for MethodConvergenceTolWithTypeContext2Mixin
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext2Mixin", "description": "Generated model for MethodConvergenceTolWithTypeContext2Mixin", "type": "object", "properties": { "convergence_tolerance": { "anyOf": [ { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext2ConvergenceTol" }, { "type": "null" } ], "argument": "value", "default": null, "description": "Stopping criterion based on objective function or statistics convergence" } }, "$defs": { "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" } }, "additionalProperties": false }
- field convergence_tolerance: MethodConvergenceTolWithTypeContext2ConvergenceTol | None = None
Stopping criterion based on objective function or statistics convergence
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext2Relative
Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext2Relative", "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field relative: Literal[True] = True
Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext3Absolute
Use absolute statistical metrics for assessing convergence in adaptive UQ methods
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext3Absolute", "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field absolute: Literal[True] = True
Use absolute statistical metrics for assessing convergence in adaptive UQ methods
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext3ConvergenceTol
Stopping criterion based on relative error reduction
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext3ConvergenceTol", "description": "Stopping criterion based on relative error reduction", "type": "object", "properties": { "value": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on relative error reduction", "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/MethodConvergenceTolWithTypeContext3Relative" }, { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext3Absolute" }, { "type": "null" } ], "default": null, "description": "Convergence tolerance type", "title": "Convergence Tolerance Type", "x-union-pattern": 2 } }, "$defs": { "MethodConvergenceTolWithTypeContext3Absolute": { "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": "MethodConvergenceTolWithTypeContext3Absolute", "type": "object" }, "MethodConvergenceTolWithTypeContext3Relative": { "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": "MethodConvergenceTolWithTypeContext3Relative", "type": "object" } }, "additionalProperties": false }
- Fields:
- field convergence_tolerance_type: MethodConvergenceTolWithTypeContext3Relative | MethodConvergenceTolWithTypeContext3Absolute | None = None
Convergence tolerance type
- field value: DakotaFloat = -1.7976931348623157e+308
Stopping criterion based on relative error reduction
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext3Mixin
Generated model for MethodConvergenceTolWithTypeContext3Mixin
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext3Mixin", "description": "Generated model for MethodConvergenceTolWithTypeContext3Mixin", "type": "object", "properties": { "convergence_tolerance": { "anyOf": [ { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext3ConvergenceTol" }, { "type": "null" } ], "argument": "value", "default": null, "description": "Stopping criterion based on relative error reduction" } }, "$defs": { "MethodConvergenceTolWithTypeContext3Absolute": { "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": "MethodConvergenceTolWithTypeContext3Absolute", "type": "object" }, "MethodConvergenceTolWithTypeContext3ConvergenceTol": { "additionalProperties": false, "description": "Stopping criterion based on relative error reduction", "properties": { "value": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on relative error reduction", "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/MethodConvergenceTolWithTypeContext3Relative" }, { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext3Absolute" }, { "type": "null" } ], "default": null, "description": "Convergence tolerance type", "title": "Convergence Tolerance Type", "x-union-pattern": 2 } }, "title": "MethodConvergenceTolWithTypeContext3ConvergenceTol", "type": "object" }, "MethodConvergenceTolWithTypeContext3Relative": { "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": "MethodConvergenceTolWithTypeContext3Relative", "type": "object" } }, "additionalProperties": false }
- field convergence_tolerance: MethodConvergenceTolWithTypeContext3ConvergenceTol | None = None
Stopping criterion based on relative error reduction
- pydantic model dakota.spec.shared.core.MethodConvergenceTolWithTypeContext3Relative
Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark
Show JSON schema
{ "title": "MethodConvergenceTolWithTypeContext3Relative", "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field relative: Literal[True] = True
Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark
- pydantic model dakota.spec.shared.core.MethodExpConvergenceToleranceMixin
Generated model for MethodExpConvergenceToleranceMixin
Show JSON schema
{ "title": "MethodExpConvergenceToleranceMixin", "description": "Generated model for MethodExpConvergenceToleranceMixin", "type": "object", "properties": { "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" } }, "$defs": { "LevelMappings": { "additionalProperties": false, "description": "Utilize the level mappings metric for guiding adaptive refinement during UQ.", "properties": { "level_mappings": { "const": true, "default": true, "description": "Utilize the level mappings metric for guiding adaptive refinement during UQ.", "title": "Level Mappings", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.expansion_refinement_metric", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "LEVEL_STATS_METRIC" } ] } }, "title": "LevelMappings", "type": "object" }, "MethodConvergenceTolWithTypeContext2Absolute": { "additionalProperties": false, "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods", "properties": { "absolute": { "const": true, "default": true, "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods", "title": "Absolute", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.convergence_tolerance_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ABSOLUTE_CONVERGENCE_TOLERANCE" } ] } }, "title": "MethodConvergenceTolWithTypeContext2Absolute", "type": "object" }, "MethodConvergenceTolWithTypeContext2ConvergenceTol": { "additionalProperties": false, "description": "Stopping criterion based on objective function or statistics convergence", "properties": { "value": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on objective function or statistics convergence", "title": "Value", "type": "number", "x-materialization": [ { "ir_key": "method.convergence_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" }, { "ir_key": "method.jega.percent_change", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "convergence_tolerance_type": { "anchor": true, "anyOf": [ { "$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" }, "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" } }, "additionalProperties": false }
- field convergence_tolerance: MethodConvergenceTolWithTypeContext2ConvergenceTol | None = None
Stopping criterion based on objective function or statistics convergence
- field refinement_metric: LevelMappings | RefinementMetricCov | None = None
Metric used for guiding adaptive refinement during UQ.
- pydantic model dakota.spec.shared.core.MethodMfExpConvergenceToleranceMixin
Generated model for MethodMfExpConvergenceToleranceMixin
Show JSON schema
{ "title": "MethodMfExpConvergenceToleranceMixin", "description": "Generated model for MethodMfExpConvergenceToleranceMixin", "type": "object", "properties": { "refinement_metric": { "anyOf": [ { "$ref": "#/$defs/LevelMappings" }, { "$ref": "#/$defs/RefinementMetricCov" }, { "type": "null" } ], "default": null, "description": "Metric used for guiding adaptive refinement during UQ.", "title": "Refinement Metric", "x-union-pattern": 2 }, "statistics_mode": { "anyOf": [ { "$ref": "#/$defs/Active" }, { "$ref": "#/$defs/StatisticsModeCombined" }, { "type": "null" } ], "default": null, "description": "type of statistical metric roll-up for multifidelity UQ methods", "title": "Statistics Mode", "x-union-pattern": 2 }, "convergence_tolerance": { "anyOf": [ { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext2ConvergenceTol" }, { "type": "null" } ], "argument": "value", "default": null, "description": "Stopping criterion based on objective function or statistics convergence" } }, "$defs": { "Active": { "additionalProperties": false, "description": "use statistics for the active expansion within multifidelity refinement", "properties": { "active": { "const": true, "default": true, "description": "use statistics for the active expansion within multifidelity refinement", "title": "Active", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.refinement_statistics_mode", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ACTIVE_EXPANSION_STATS" } ] } }, "title": "Active", "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" }, "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" }, "StatisticsModeCombined": { "additionalProperties": false, "description": "use statistics for the combined expansion within multifidelity refinement", "properties": { "combined": { "const": true, "default": true, "description": "use statistics for the combined expansion within multifidelity refinement", "title": "Combined", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.refinement_statistics_mode", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMBINED_EXPANSION_STATS" } ] } }, "title": "StatisticsModeCombined", "type": "object" } }, "additionalProperties": false }
- field convergence_tolerance: MethodConvergenceTolWithTypeContext2ConvergenceTol | None = None
Stopping criterion based on objective function or statistics convergence
- field refinement_metric: LevelMappings | RefinementMetricCov | None = None
Metric used for guiding adaptive refinement during UQ.
- field statistics_mode: Active | StatisticsModeCombined | None = None
type of statistical metric roll-up for multifidelity UQ methods
- pydantic model dakota.spec.shared.core.MethodMfRefineStatsModeMixin
Generated model for MethodMfRefineStatsModeMixin
Show JSON schema
{ "title": "MethodMfRefineStatsModeMixin", "description": "Generated model for MethodMfRefineStatsModeMixin", "type": "object", "properties": { "statistics_mode": { "anyOf": [ { "$ref": "#/$defs/Active" }, { "$ref": "#/$defs/StatisticsModeCombined" }, { "type": "null" } ], "default": null, "description": "type of statistical metric roll-up for multifidelity UQ methods", "title": "Statistics Mode", "x-union-pattern": 2 } }, "$defs": { "Active": { "additionalProperties": false, "description": "use statistics for the active expansion within multifidelity refinement", "properties": { "active": { "const": true, "default": true, "description": "use statistics for the active expansion within multifidelity refinement", "title": "Active", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.refinement_statistics_mode", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ACTIVE_EXPANSION_STATS" } ] } }, "title": "Active", "type": "object" }, "StatisticsModeCombined": { "additionalProperties": false, "description": "use statistics for the combined expansion within multifidelity refinement", "properties": { "combined": { "const": true, "default": true, "description": "use statistics for the combined expansion within multifidelity refinement", "title": "Combined", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.refinement_statistics_mode", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMBINED_EXPANSION_STATS" } ] } }, "title": "StatisticsModeCombined", "type": "object" } }, "additionalProperties": false }
- field statistics_mode: Active | StatisticsModeCombined | None = None
type of statistical metric roll-up for multifidelity UQ methods
- pydantic model dakota.spec.shared.core.MethodRefineMetricTypeMixin
Generated model for MethodRefineMetricTypeMixin
Show JSON schema
{ "title": "MethodRefineMetricTypeMixin", "description": "Generated model for MethodRefineMetricTypeMixin", "type": "object", "properties": { "refinement_metric": { "anyOf": [ { "$ref": "#/$defs/LevelMappings" }, { "$ref": "#/$defs/RefinementMetricCov" }, { "type": "null" } ], "default": null, "description": "Metric used for guiding adaptive refinement during UQ.", "title": "Refinement Metric", "x-union-pattern": 2 } }, "$defs": { "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" }, "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" } }, "additionalProperties": false }
- field refinement_metric: LevelMappings | RefinementMetricCov | None = None
Metric used for guiding adaptive refinement during UQ.
- pydantic model dakota.spec.shared.core.NumericalGradientOptionsIntervalTypeCentral
Use central differences
Show JSON schema
{ "title": "NumericalGradientOptionsIntervalTypeCentral", "description": "Use central differences", "type": "object", "properties": { "central": { "const": true, "default": true, "description": "Use central differences", "title": "Central", "type": "boolean", "x-materialization": [ { "ir_key": "responses.interval_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "central" } ] } }, "additionalProperties": false }
- Fields:
- field central: Literal[True] = True
Use central differences
- pydantic model dakota.spec.shared.core.NumericalGradientOptionsIntervalTypeForward
(Default) Use forward differences
Show JSON schema
{ "title": "NumericalGradientOptionsIntervalTypeForward", "description": "(Default) Use forward differences", "type": "object", "properties": { "forward": { "const": true, "default": true, "description": "(Default) Use forward differences", "title": "Forward", "type": "boolean", "x-materialization": [ { "ir_key": "responses.interval_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "forward" } ] } }, "additionalProperties": false }
- Fields:
- field forward: Literal[True] = True
(Default) Use forward differences
- pydantic model dakota.spec.shared.core.NumericalGradientOptionsMethodSourceDakota
(Default) Use internal Dakota finite differences algorithm
Show JSON schema
{ "title": "NumericalGradientOptionsMethodSourceDakota", "description": "(Default) Use internal Dakota finite differences algorithm", "type": "object", "properties": { "dakota": { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaConfig", "x-materialization": [ { "ir_key": "responses.method_source", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "dakota" } ], "x-model-default": "NumericalGradientOptionsMethodSourceDakotaConfig" } }, "$defs": { "NumericalGradientOptionsMethodSourceDakotaAbsolute": { "additionalProperties": false, "description": "Do not scale step-size", "properties": { "absolute": { "const": true, "default": true, "description": "Do not scale step-size", "title": "Absolute", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "absolute" } ] } }, "title": "NumericalGradientOptionsMethodSourceDakotaAbsolute", "type": "object" }, "NumericalGradientOptionsMethodSourceDakotaBounds": { "additionalProperties": false, "description": "Scale step-size by the domain of the parameter", "properties": { "bounds": { "const": true, "default": true, "description": "Scale step-size by the domain of the parameter", "title": "Bounds", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "bounds" } ] } }, "title": "NumericalGradientOptionsMethodSourceDakotaBounds", "type": "object" }, "NumericalGradientOptionsMethodSourceDakotaConfig": { "additionalProperties": false, "description": "(Default) Use internal Dakota finite differences algorithm", "properties": { "ignore_bounds": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Do not respect bounds when computing gradients or Hessians", "title": "Ignore Bounds", "x-materialization": [ { "ir_key": "responses.ignore_bounds", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "step_scaling": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaRelative" }, { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaAbsolute" }, { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaBounds" } ], "description": "Step Scaling", "title": "Step Scaling", "x-model-default": "NumericalGradientOptionsMethodSourceDakotaRelative", "x-union-pattern": 1 } }, "title": "NumericalGradientOptionsMethodSourceDakotaConfig", "type": "object" }, "NumericalGradientOptionsMethodSourceDakotaRelative": { "additionalProperties": false, "description": "(Default) Scale step size by the parameter value", "properties": { "relative": { "const": true, "default": true, "description": "(Default) Scale step size by the parameter value", "title": "Relative", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "relative" } ] } }, "title": "NumericalGradientOptionsMethodSourceDakotaRelative", "type": "object" } }, "additionalProperties": false }
- field dakota: NumericalGradientOptionsMethodSourceDakotaConfig [Optional]
(Default) Use internal Dakota finite differences algorithm
- pydantic model dakota.spec.shared.core.NumericalGradientOptionsMethodSourceDakotaAbsolute
Do not scale step-size
Show JSON schema
{ "title": "NumericalGradientOptionsMethodSourceDakotaAbsolute", "description": "Do not scale step-size", "type": "object", "properties": { "absolute": { "const": true, "default": true, "description": "Do not scale step-size", "title": "Absolute", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "absolute" } ] } }, "additionalProperties": false }
- Fields:
- field absolute: Literal[True] = True
Do not scale step-size
- pydantic model dakota.spec.shared.core.NumericalGradientOptionsMethodSourceDakotaBounds
Scale step-size by the domain of the parameter
Show JSON schema
{ "title": "NumericalGradientOptionsMethodSourceDakotaBounds", "description": "Scale step-size by the domain of the parameter", "type": "object", "properties": { "bounds": { "const": true, "default": true, "description": "Scale step-size by the domain of the parameter", "title": "Bounds", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "bounds" } ] } }, "additionalProperties": false }
- Fields:
- field bounds: Literal[True] = True
Scale step-size by the domain of the parameter
- pydantic model dakota.spec.shared.core.NumericalGradientOptionsMethodSourceDakotaConfig
(Default) Use internal Dakota finite differences algorithm
Show JSON schema
{ "title": "NumericalGradientOptionsMethodSourceDakotaConfig", "description": "(Default) Use internal Dakota finite differences algorithm", "type": "object", "properties": { "ignore_bounds": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Do not respect bounds when computing gradients or Hessians", "title": "Ignore Bounds", "x-materialization": [ { "ir_key": "responses.ignore_bounds", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "step_scaling": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaRelative" }, { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaAbsolute" }, { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaBounds" } ], "description": "Step Scaling", "title": "Step Scaling", "x-model-default": "NumericalGradientOptionsMethodSourceDakotaRelative", "x-union-pattern": 1 } }, "$defs": { "NumericalGradientOptionsMethodSourceDakotaAbsolute": { "additionalProperties": false, "description": "Do not scale step-size", "properties": { "absolute": { "const": true, "default": true, "description": "Do not scale step-size", "title": "Absolute", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "absolute" } ] } }, "title": "NumericalGradientOptionsMethodSourceDakotaAbsolute", "type": "object" }, "NumericalGradientOptionsMethodSourceDakotaBounds": { "additionalProperties": false, "description": "Scale step-size by the domain of the parameter", "properties": { "bounds": { "const": true, "default": true, "description": "Scale step-size by the domain of the parameter", "title": "Bounds", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "bounds" } ] } }, "title": "NumericalGradientOptionsMethodSourceDakotaBounds", "type": "object" }, "NumericalGradientOptionsMethodSourceDakotaRelative": { "additionalProperties": false, "description": "(Default) Scale step size by the parameter value", "properties": { "relative": { "const": true, "default": true, "description": "(Default) Scale step size by the parameter value", "title": "Relative", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "relative" } ] } }, "title": "NumericalGradientOptionsMethodSourceDakotaRelative", "type": "object" } }, "additionalProperties": false }
- Fields:
- field ignore_bounds: Literal[True] | None = None
Do not respect bounds when computing gradients or Hessians
- field step_scaling: NumericalGradientOptionsMethodSourceDakotaRelative | NumericalGradientOptionsMethodSourceDakotaAbsolute | NumericalGradientOptionsMethodSourceDakotaBounds [Optional]
Step Scaling
- pydantic model dakota.spec.shared.core.NumericalGradientOptionsMethodSourceDakotaRelative
(Default) Scale step size by the parameter value
Show JSON schema
{ "title": "NumericalGradientOptionsMethodSourceDakotaRelative", "description": "(Default) Scale step size by the parameter value", "type": "object", "properties": { "relative": { "const": true, "default": true, "description": "(Default) Scale step size by the parameter value", "title": "Relative", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "relative" } ] } }, "additionalProperties": false }
- Fields:
- field relative: Literal[True] = True
(Default) Scale step size by the parameter value
- pydantic model dakota.spec.shared.core.NumericalGradientOptionsMethodSourceVendor
Use non-Dakota fd algorithm
Show JSON schema
{ "title": "NumericalGradientOptionsMethodSourceVendor", "description": "Use non-Dakota fd algorithm", "type": "object", "properties": { "vendor": { "const": true, "default": true, "description": "Use non-Dakota fd algorithm", "title": "Vendor", "type": "boolean", "x-materialization": [ { "ir_key": "responses.method_source", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "vendor" } ] } }, "additionalProperties": false }
- Fields:
- field vendor: Literal[True] = True
Use non-Dakota fd algorithm
- pydantic model dakota.spec.shared.core.NumericalGradientOptionsMixin
Generated model for NumericalGradientOptionsMixin
Show JSON schema
{ "title": "NumericalGradientOptionsMixin", "description": "Generated model for NumericalGradientOptionsMixin", "type": "object", "properties": { "method_source": { "anyOf": [ { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakota" }, { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceVendor" } ], "description": "Specify which finite difference routine is used", "title": "Method Source", "x-model-default": "NumericalGradientOptionsMethodSourceDakota", "x-union-pattern": 1 }, "interval_type": { "anyOf": [ { "$ref": "#/$defs/NumericalGradientOptionsIntervalTypeForward" }, { "$ref": "#/$defs/NumericalGradientOptionsIntervalTypeCentral" } ], "description": "Specify how to compute gradients and hessians", "title": "Interval Type", "x-model-default": "NumericalGradientOptionsIntervalTypeForward", "x-union-pattern": 1 }, "fd_step_size": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Step size used when computing gradients and Hessians", "title": "Fd Step Size", "x-aliases": [ "fd_gradient_step_size" ], "x-materialization": [ { "ir_key": "responses.fd_gradient_step_size", "ir_value_type": "RealVector", "storage_type": "DIRECT_VALUE" } ] } }, "$defs": { "NumericalGradientOptionsIntervalTypeCentral": { "additionalProperties": false, "description": "Use central differences", "properties": { "central": { "const": true, "default": true, "description": "Use central differences", "title": "Central", "type": "boolean", "x-materialization": [ { "ir_key": "responses.interval_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "central" } ] } }, "title": "NumericalGradientOptionsIntervalTypeCentral", "type": "object" }, "NumericalGradientOptionsIntervalTypeForward": { "additionalProperties": false, "description": "(Default) Use forward differences", "properties": { "forward": { "const": true, "default": true, "description": "(Default) Use forward differences", "title": "Forward", "type": "boolean", "x-materialization": [ { "ir_key": "responses.interval_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "forward" } ] } }, "title": "NumericalGradientOptionsIntervalTypeForward", "type": "object" }, "NumericalGradientOptionsMethodSourceDakota": { "additionalProperties": false, "description": "(Default) Use internal Dakota finite differences algorithm", "properties": { "dakota": { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaConfig", "x-materialization": [ { "ir_key": "responses.method_source", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "dakota" } ], "x-model-default": "NumericalGradientOptionsMethodSourceDakotaConfig" } }, "title": "NumericalGradientOptionsMethodSourceDakota", "type": "object" }, "NumericalGradientOptionsMethodSourceDakotaAbsolute": { "additionalProperties": false, "description": "Do not scale step-size", "properties": { "absolute": { "const": true, "default": true, "description": "Do not scale step-size", "title": "Absolute", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "absolute" } ] } }, "title": "NumericalGradientOptionsMethodSourceDakotaAbsolute", "type": "object" }, "NumericalGradientOptionsMethodSourceDakotaBounds": { "additionalProperties": false, "description": "Scale step-size by the domain of the parameter", "properties": { "bounds": { "const": true, "default": true, "description": "Scale step-size by the domain of the parameter", "title": "Bounds", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "bounds" } ] } }, "title": "NumericalGradientOptionsMethodSourceDakotaBounds", "type": "object" }, "NumericalGradientOptionsMethodSourceDakotaConfig": { "additionalProperties": false, "description": "(Default) Use internal Dakota finite differences algorithm", "properties": { "ignore_bounds": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Do not respect bounds when computing gradients or Hessians", "title": "Ignore Bounds", "x-materialization": [ { "ir_key": "responses.ignore_bounds", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "step_scaling": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaRelative" }, { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaAbsolute" }, { "$ref": "#/$defs/NumericalGradientOptionsMethodSourceDakotaBounds" } ], "description": "Step Scaling", "title": "Step Scaling", "x-model-default": "NumericalGradientOptionsMethodSourceDakotaRelative", "x-union-pattern": 1 } }, "title": "NumericalGradientOptionsMethodSourceDakotaConfig", "type": "object" }, "NumericalGradientOptionsMethodSourceDakotaRelative": { "additionalProperties": false, "description": "(Default) Scale step size by the parameter value", "properties": { "relative": { "const": true, "default": true, "description": "(Default) Scale step size by the parameter value", "title": "Relative", "type": "boolean", "x-materialization": [ { "ir_key": "responses.fd_gradient_step_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "relative" } ] } }, "title": "NumericalGradientOptionsMethodSourceDakotaRelative", "type": "object" }, "NumericalGradientOptionsMethodSourceVendor": { "additionalProperties": false, "description": "Use non-Dakota fd algorithm", "properties": { "vendor": { "const": true, "default": true, "description": "Use non-Dakota fd algorithm", "title": "Vendor", "type": "boolean", "x-materialization": [ { "ir_key": "responses.method_source", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "vendor" } ] } }, "title": "NumericalGradientOptionsMethodSourceVendor", "type": "object" } }, "additionalProperties": false }
- Fields:
- field fd_step_size: list[DakotaFloat] | None = None
Step size used when computing gradients and Hessians
- field interval_type: NumericalGradientOptionsIntervalTypeForward | NumericalGradientOptionsIntervalTypeCentral [Optional]
Specify how to compute gradients and hessians
- field method_source: NumericalGradientOptionsMethodSourceDakota | NumericalGradientOptionsMethodSourceVendor [Optional]
Specify which finite difference routine is used
- pydantic model dakota.spec.shared.core.RefinementMetricCov
Utilize the response covariance metric for guiding adaptive refinement during UQ.
Show JSON schema
{ "title": "RefinementMetricCov", "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field covariance: Literal[True] = True
Utilize the response covariance metric for guiding adaptive refinement during UQ.
- pydantic model dakota.spec.shared.core.ResponseLevelsComputeProbRelGenCompute
Selection of statistics to compute at each response level
Show JSON schema
{ "title": "ResponseLevelsComputeProbRelGenCompute", "description": "Selection of statistics to compute at each response level", "type": "object", "properties": { "statistic": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenProbabilities" }, { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenReliabilities" }, { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenGenReliabilities" } ], "description": "Statistics to Compute", "title": "Statistic", "x-union-pattern": 4 }, "system": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenSystemSeries" }, { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenSystemParallel" }, { "type": "null" } ], "default": null, "description": "Compute system reliability (series or parallel)", "title": "System", "x-union-pattern": 2 } }, "$defs": { "ResponseLevelsComputeProbRelGenGenReliabilities": { "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": "ResponseLevelsComputeProbRelGenGenReliabilities", "type": "object" }, "ResponseLevelsComputeProbRelGenProbabilities": { "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": "ResponseLevelsComputeProbRelGenProbabilities", "type": "object" }, "ResponseLevelsComputeProbRelGenReliabilities": { "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": "ResponseLevelsComputeProbRelGenReliabilities", "type": "object" }, "ResponseLevelsComputeProbRelGenSystemParallel": { "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": "ResponseLevelsComputeProbRelGenSystemParallel", "type": "object" }, "ResponseLevelsComputeProbRelGenSystemSeries": { "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": "ResponseLevelsComputeProbRelGenSystemSeries", "type": "object" } }, "additionalProperties": false, "required": [ "statistic" ] }
- Fields:
- field statistic: ResponseLevelsComputeProbRelGenProbabilities | ResponseLevelsComputeProbRelGenReliabilities | ResponseLevelsComputeProbRelGenGenReliabilities [Required]
Statistics to Compute
- field system: ResponseLevelsComputeProbRelGenSystemSeries | ResponseLevelsComputeProbRelGenSystemParallel | None = None
Compute system reliability (series or parallel)
- pydantic model dakota.spec.shared.core.ResponseLevelsComputeProbRelGenGenReliabilities
Computes generalized reliabilities associated with response levels
Show JSON schema
{ "title": "ResponseLevelsComputeProbRelGenGenReliabilities", "description": "Computes generalized reliabilities associated with response levels", "type": "object", "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" } ] } }, "additionalProperties": false }
- field gen_reliabilities: Literal[True] = True
Computes generalized reliabilities associated with response levels
- pydantic model dakota.spec.shared.core.ResponseLevelsComputeProbRelGenMixin
Generated model for ResponseLevelsComputeProbRelGenMixin
Show JSON schema
{ "title": "ResponseLevelsComputeProbRelGenMixin", "description": "Generated model for ResponseLevelsComputeProbRelGenMixin", "type": "object", "properties": { "response_levels": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenResponseLevels" }, { "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" } ] } }, "$defs": { "ResponseLevelsComputeProbRelGenCompute": { "additionalProperties": false, "description": "Selection of statistics to compute at each response level", "properties": { "statistic": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenProbabilities" }, { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenReliabilities" }, { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenGenReliabilities" } ], "description": "Statistics to Compute", "title": "Statistic", "x-union-pattern": 4 }, "system": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenSystemSeries" }, { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenSystemParallel" }, { "type": "null" } ], "default": null, "description": "Compute system reliability (series or parallel)", "title": "System", "x-union-pattern": 2 } }, "required": [ "statistic" ], "title": "ResponseLevelsComputeProbRelGenCompute", "type": "object" }, "ResponseLevelsComputeProbRelGenGenReliabilities": { "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": "ResponseLevelsComputeProbRelGenGenReliabilities", "type": "object" }, "ResponseLevelsComputeProbRelGenProbabilities": { "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": "ResponseLevelsComputeProbRelGenProbabilities", "type": "object" }, "ResponseLevelsComputeProbRelGenReliabilities": { "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": "ResponseLevelsComputeProbRelGenReliabilities", "type": "object" }, "ResponseLevelsComputeProbRelGenResponseLevels": { "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/ResponseLevelsComputeProbRelGenCompute" }, { "type": "null" } ], "default": null, "description": "Selection of statistics to compute at each response level" } }, "required": [ "values" ], "title": "ResponseLevelsComputeProbRelGenResponseLevels", "type": "object", "x-model-validations": [ { "validationContext": "responselevelscomputeprobrelgenresponselevels", "validationErrorMessage": "For responselevelscomputeprobrelgenresponselevels, sum of num_response_levels must equal length of values.", "validationFields": [ "num_response_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ResponseLevelsComputeProbRelGenSystemParallel": { "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": "ResponseLevelsComputeProbRelGenSystemParallel", "type": "object" }, "ResponseLevelsComputeProbRelGenSystemSeries": { "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": "ResponseLevelsComputeProbRelGenSystemSeries", "type": "object" } }, "additionalProperties": false }
- Fields:
- field response_levels: ResponseLevelsComputeProbRelGenResponseLevels | None = None
Values at which to estimate desired statistics for each response
- pydantic model dakota.spec.shared.core.ResponseLevelsComputeProbRelGenProbabilities
Computes probabilities associated with response levels
Show JSON schema
{ "title": "ResponseLevelsComputeProbRelGenProbabilities", "description": "Computes probabilities associated with response levels", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field probabilities: Literal[True] = True
Computes probabilities associated with response levels
- pydantic model dakota.spec.shared.core.ResponseLevelsComputeProbRelGenReliabilities
Computes reliabilities associated with response levels
Show JSON schema
{ "title": "ResponseLevelsComputeProbRelGenReliabilities", "description": "Computes reliabilities associated with response levels", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field reliabilities: Literal[True] = True
Computes reliabilities associated with response levels
- pydantic model dakota.spec.shared.core.ResponseLevelsComputeProbRelGenResponseLevels
Values at which to estimate desired statistics for each response
Show JSON schema
{ "title": "ResponseLevelsComputeProbRelGenResponseLevels", "description": "Values at which to estimate desired statistics for each response", "type": "object", "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": "#/$defs/ResponseLevelsComputeProbRelGenCompute" }, { "type": "null" } ], "default": null, "description": "Selection of statistics to compute at each response level" } }, "$defs": { "ResponseLevelsComputeProbRelGenCompute": { "additionalProperties": false, "description": "Selection of statistics to compute at each response level", "properties": { "statistic": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenProbabilities" }, { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenReliabilities" }, { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenGenReliabilities" } ], "description": "Statistics to Compute", "title": "Statistic", "x-union-pattern": 4 }, "system": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenSystemSeries" }, { "$ref": "#/$defs/ResponseLevelsComputeProbRelGenSystemParallel" }, { "type": "null" } ], "default": null, "description": "Compute system reliability (series or parallel)", "title": "System", "x-union-pattern": 2 } }, "required": [ "statistic" ], "title": "ResponseLevelsComputeProbRelGenCompute", "type": "object" }, "ResponseLevelsComputeProbRelGenGenReliabilities": { "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": "ResponseLevelsComputeProbRelGenGenReliabilities", "type": "object" }, "ResponseLevelsComputeProbRelGenProbabilities": { "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": "ResponseLevelsComputeProbRelGenProbabilities", "type": "object" }, "ResponseLevelsComputeProbRelGenReliabilities": { "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": "ResponseLevelsComputeProbRelGenReliabilities", "type": "object" }, "ResponseLevelsComputeProbRelGenSystemParallel": { "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": "ResponseLevelsComputeProbRelGenSystemParallel", "type": "object" }, "ResponseLevelsComputeProbRelGenSystemSeries": { "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": "ResponseLevelsComputeProbRelGenSystemSeries", "type": "object" } }, "additionalProperties": false, "required": [ "values" ], "x-model-validations": [ { "validationContext": "responselevelscomputeprobrelgenresponselevels", "validationErrorMessage": "For responselevelscomputeprobrelgenresponselevels, sum of num_response_levels must equal length of values.", "validationFields": [ "num_response_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }
- Fields:
- field compute: ResponseLevelsComputeProbRelGenCompute | None = None
Selection of statistics to compute at each response level
- field num_response_levels: list[int] | None = None
Number of values at which to estimate desired statistics for each response
- field values: list[DakotaFloat] [Required]
Values at which to estimate desired statistics for each response
- pydantic model dakota.spec.shared.core.ResponseLevelsComputeProbRelGenSystemParallel
Aggregate response statistics assuming a parallel system
Show JSON schema
{ "title": "ResponseLevelsComputeProbRelGenSystemParallel", "description": "Aggregate response statistics assuming a parallel system", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field parallel: Literal[True] = True
Aggregate response statistics assuming a parallel system
- pydantic model dakota.spec.shared.core.ResponseLevelsComputeProbRelGenSystemSeries
Aggregate response statistics assuming a series system
Show JSON schema
{ "title": "ResponseLevelsComputeProbRelGenSystemSeries", "description": "Aggregate response statistics assuming a series system", "type": "object", "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" } ] } }, "additionalProperties": false }
- Fields:
- field series: Literal[True] = True
Aggregate response statistics assuming a series system
- pydantic model dakota.spec.shared.core.StatisticsModeCombined
use statistics for the combined expansion within multifidelity refinement
Show JSON schema
{ "title": "StatisticsModeCombined", "description": "use statistics for the combined expansion within multifidelity refinement", "type": "object", "properties": { "combined": { "const": true, "default": true, "description": "use statistics for the combined expansion within multifidelity refinement", "title": "Combined", "type": "boolean", "x-materialization": [ { "enum_scope": "Pecos", "ir_key": "method.nond.refinement_statistics_mode", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMBINED_EXPANSION_STATS" } ] } }, "additionalProperties": false }
- Fields:
- field combined: Literal[True] = True
use statistics for the combined expansion within multifidelity refinement
