Sampling

Generated Pydantic models for shared.sampling

pydantic model dakota.spec.shared.sampling.AutoReorder

Reorder models automatically

Show JSON schema

{
   "title": "AutoReorder",
   "description": "Reorder models automatically",
   "type": "object",
   "properties": {
      "auto_reorder": {
         "const": true,
         "default": true,
         "description": "Reorder models automatically",
         "title": "Auto Reorder",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.model_reordering",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "REORDER_MODELS_ON_THE_FLY"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

auto_reorder (Literal[True])

field auto_reorder: Literal[True] = True: Reorder models automatically

pydantic model dakota.spec.shared.sampling.Fallback

Fall back to a numerical solve when needed for mitigation in MFMC

Show JSON schema

{
   "title": "Fallback",
   "description": "Fall back to a numerical solve when needed for mitigation in MFMC",
   "type": "object",
   "properties": {
      "fallback": {
         "const": true,
         "default": true,
         "description": "Fall back to a numerical solve when needed for mitigation in MFMC",
         "title": "Fallback",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.numerical_solve_mode",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "NUMERICAL_FALLBACK"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

fallback (Literal[True])

field fallback: Literal[True] = True: Fall back to a numerical solve when needed for mitigation in MFMC

pydantic model dakota.spec.shared.sampling.FixedOrder

Used a fixed model order

Show JSON schema

{
   "title": "FixedOrder",
   "description": "Used a fixed model order",
   "type": "object",
   "properties": {
      "fixed_order": {
         "const": true,
         "default": true,
         "description": "Used a fixed model order",
         "title": "Fixed Order",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.model_reordering",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "FIXED_MODEL_ORDERING"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

fixed_order (Literal[True])

field fixed_order: Literal[True] = True: Used a fixed model order

pydantic model dakota.spec.shared.sampling.MethodExportSamplesFormatAnnotated

Selects annotated tabular file format

Show JSON schema

{
   "title": "MethodExportSamplesFormatAnnotated",
   "description": "Selects annotated tabular file format",
   "type": "object",
   "properties": {
      "annotated": {
         "const": true,
         "default": true,
         "description": "Selects annotated tabular file format",
         "title": "Annotated",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.export_samples_format",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "TABULAR_ANNOTATED"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

annotated (Literal[True])

field annotated: Literal[True] = True: Selects annotated tabular file format

pydantic model dakota.spec.shared.sampling.MethodExportSamplesFormatCustomAnnotated

Selects custom-annotated tabular file format

Show JSON schema

{
   "title": "MethodExportSamplesFormatCustomAnnotated",
   "description": "Selects custom-annotated tabular file format",
   "type": "object",
   "properties": {
      "custom_annotated": {
         "$ref": "#/$defs/MethodExportSamplesFormatCustomAnnotatedConfig",
         "x-materialization": [
            {
               "ir_key": "method.nond.export_samples_format",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "TABULAR_NONE"
            }
         ],
         "x-model-default": "MethodExportSamplesFormatCustomAnnotatedConfig"
      }
   },
   "$defs": {
      "MethodExportSamplesFormatCustomAnnotatedConfig": {
         "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.nond.export_samples_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.nond.export_samples_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.nond.export_samples_format",
                     "ir_value_type": "unsigned short",
                     "storage_type": "AUGMENT_ENUM",
                     "stored_value": "TABULAR_IFACE_ID"
                  }
               ]
            }
         },
         "title": "MethodExportSamplesFormatCustomAnnotatedConfig",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Fields:

custom_annotated (dakota.spec.shared.sampling.MethodExportSamplesFormatCustomAnnotatedConfig)

field custom_annotated: MethodExportSamplesFormatCustomAnnotatedConfig [Optional]: Selects custom-annotated tabular file format

pydantic model dakota.spec.shared.sampling.MethodExportSamplesFormatCustomAnnotatedConfig

Selects custom-annotated tabular file format

Show JSON schema

{
   "title": "MethodExportSamplesFormatCustomAnnotatedConfig",
   "description": "Selects custom-annotated tabular file format",
   "type": "object",
   "properties": {
      "header": {
         "anyOf": [
            {
               "const": true,
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Enable header row in custom-annotated tabular file",
         "title": "Header",
         "x-materialization": [
            {
               "ir_key": "method.nond.export_samples_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.nond.export_samples_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.nond.export_samples_format",
               "ir_value_type": "unsigned short",
               "storage_type": "AUGMENT_ENUM",
               "stored_value": "TABULAR_IFACE_ID"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

eval_id (Literal[True] | None)
header (Literal[True] | None)
interface_id (Literal[True] | None)

field eval_id: Literal[True] | None = None: Enable evaluation ID column in custom-annotated tabular file

field header: Literal[True] | None = None: Enable header row in custom-annotated tabular file

field interface_id: Literal[True] | None = None: Enable interface ID column in custom-annotated tabular file

pydantic model dakota.spec.shared.sampling.MethodExportSamplesFormatFreeform

Selects freeform file format

Show JSON schema

{
   "title": "MethodExportSamplesFormatFreeform",
   "description": "Selects freeform file format",
   "type": "object",
   "properties": {
      "freeform": {
         "const": true,
         "default": true,
         "description": "Selects freeform file format",
         "title": "Freeform",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.export_samples_format",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "TABULAR_NONE"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

freeform (Literal[True])

field freeform: Literal[True] = True: Selects freeform file format

pydantic model dakota.spec.shared.sampling.MethodExportSamplesFormatMixin

Generated model for MethodExportSamplesFormatMixin

Show JSON schema

{
   "title": "MethodExportSamplesFormatMixin",
   "description": "Generated model for MethodExportSamplesFormatMixin",
   "type": "object",
   "properties": {
      "format": {
         "anchor": true,
         "anyOf": [
            {
               "$ref": "#/$defs/MethodExportSamplesFormatCustomAnnotated"
            },
            {
               "$ref": "#/$defs/MethodExportSamplesFormatAnnotated"
            },
            {
               "$ref": "#/$defs/MethodExportSamplesFormatFreeform"
            }
         ],
         "description": "Tabular Format",
         "title": "Format",
         "x-model-default": "MethodExportSamplesFormatAnnotated",
         "x-union-pattern": 1
      }
   },
   "$defs": {
      "MethodExportSamplesFormatAnnotated": {
         "additionalProperties": false,
         "description": "Selects annotated tabular file format",
         "properties": {
            "annotated": {
               "const": true,
               "default": true,
               "description": "Selects annotated tabular file format",
               "title": "Annotated",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.export_samples_format",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TABULAR_ANNOTATED"
                  }
               ]
            }
         },
         "title": "MethodExportSamplesFormatAnnotated",
         "type": "object"
      },
      "MethodExportSamplesFormatCustomAnnotated": {
         "additionalProperties": false,
         "description": "Selects custom-annotated tabular file format",
         "properties": {
            "custom_annotated": {
               "$ref": "#/$defs/MethodExportSamplesFormatCustomAnnotatedConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.export_samples_format",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TABULAR_NONE"
                  }
               ],
               "x-model-default": "MethodExportSamplesFormatCustomAnnotatedConfig"
            }
         },
         "title": "MethodExportSamplesFormatCustomAnnotated",
         "type": "object"
      },
      "MethodExportSamplesFormatCustomAnnotatedConfig": {
         "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.nond.export_samples_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.nond.export_samples_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.nond.export_samples_format",
                     "ir_value_type": "unsigned short",
                     "storage_type": "AUGMENT_ENUM",
                     "stored_value": "TABULAR_IFACE_ID"
                  }
               ]
            }
         },
         "title": "MethodExportSamplesFormatCustomAnnotatedConfig",
         "type": "object"
      },
      "MethodExportSamplesFormatFreeform": {
         "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.nond.export_samples_format",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TABULAR_NONE"
                  }
               ]
            }
         },
         "title": "MethodExportSamplesFormatFreeform",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Fields:

format (dakota.spec.shared.sampling.MethodExportSamplesFormatCustomAnnotated | dakota.spec.shared.sampling.MethodExportSamplesFormatAnnotated | dakota.spec.shared.sampling.MethodExportSamplesFormatFreeform)

field format: MethodExportSamplesFormatCustomAnnotated | MethodExportSamplesFormatAnnotated | MethodExportSamplesFormatFreeform [Optional]: Tabular Format

pydantic model dakota.spec.shared.sampling.MethodSampleTypeLhsMcLhs

Uses Latin Hypercube Sampling (LHS) to sample variables

Show JSON schema

{
   "title": "MethodSampleTypeLhsMcLhs",
   "description": "Uses Latin Hypercube Sampling (LHS) to sample variables",
   "type": "object",
   "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"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

lhs (Literal[True])

field lhs: Literal[True] = True: Uses Latin Hypercube Sampling (LHS) to sample variables

pydantic model dakota.spec.shared.sampling.MethodSampleTypeLhsMcMixin

Generated model for MethodSampleTypeLhsMcMixin

Show JSON schema

{
   "title": "MethodSampleTypeLhsMcMixin",
   "description": "Generated model for MethodSampleTypeLhsMcMixin",
   "type": "object",
   "properties": {
      "sample_type": {
         "anyOf": [
            {
               "$ref": "#/$defs/MethodSampleTypeLhsMcLhs"
            },
            {
               "$ref": "#/$defs/MethodSampleTypeLhsMcRandom"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Selection of sampling strategy",
         "title": "Sample Type",
         "x-union-pattern": 2
      }
   },
   "$defs": {
      "MethodSampleTypeLhsMcLhs": {
         "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": "MethodSampleTypeLhsMcLhs",
         "type": "object"
      },
      "MethodSampleTypeLhsMcRandom": {
         "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": "MethodSampleTypeLhsMcRandom",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Fields:

sample_type (dakota.spec.shared.sampling.MethodSampleTypeLhsMcLhs | dakota.spec.shared.sampling.MethodSampleTypeLhsMcRandom | None)

field sample_type: MethodSampleTypeLhsMcLhs | MethodSampleTypeLhsMcRandom | None = None: Selection of sampling strategy

pydantic model dakota.spec.shared.sampling.MethodSampleTypeLhsMcRandom

Uses purely random Monte Carlo sampling to sample variables

Show JSON schema

{
   "title": "MethodSampleTypeLhsMcRandom",
   "description": "Uses purely random Monte Carlo sampling to sample variables",
   "type": "object",
   "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"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

random (Literal[True])

field random: Literal[True] = True: Uses purely random Monte Carlo sampling to sample variables

pydantic model dakota.spec.shared.sampling.MfmcNumericalSolveMixin

Generated model for MfmcNumericalSolveMixin

Show JSON schema

{
   "title": "MfmcNumericalSolveMixin",
   "description": "Generated model for MfmcNumericalSolveMixin",
   "type": "object",
   "properties": {
      "numerical_solve": {
         "anyOf": [
            {
               "$ref": "#/$defs/NumericalSolve"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Specify the situations where numerical optimization is used for MFMC sample allocation"
      }
   },
   "$defs": {
      "AutoReorder": {
         "additionalProperties": false,
         "description": "Reorder models automatically",
         "properties": {
            "auto_reorder": {
               "const": true,
               "default": true,
               "description": "Reorder models automatically",
               "title": "Auto Reorder",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.model_reordering",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "REORDER_MODELS_ON_THE_FLY"
                  }
               ]
            }
         },
         "title": "AutoReorder",
         "type": "object"
      },
      "Fallback": {
         "additionalProperties": false,
         "description": "Fall back to a numerical solve when needed for mitigation in MFMC",
         "properties": {
            "fallback": {
               "const": true,
               "default": true,
               "description": "Fall back to a numerical solve when needed for mitigation in MFMC",
               "title": "Fallback",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.numerical_solve_mode",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NUMERICAL_FALLBACK"
                  }
               ]
            }
         },
         "title": "Fallback",
         "type": "object"
      },
      "FixedOrder": {
         "additionalProperties": false,
         "description": "Used a fixed model order",
         "properties": {
            "fixed_order": {
               "const": true,
               "default": true,
               "description": "Used a fixed model order",
               "title": "Fixed Order",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.model_reordering",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "FIXED_MODEL_ORDERING"
                  }
               ]
            }
         },
         "title": "FixedOrder",
         "type": "object"
      },
      "NumericalSolve": {
         "additionalProperties": false,
         "description": "Specify the situations where numerical optimization is used for MFMC sample allocation",
         "properties": {
            "numerical_solve_strategy": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/Fallback"
                  },
                  {
                     "$ref": "#/$defs/Override"
                  }
               ],
               "description": "Employ numerical solve",
               "title": "Numerical Solve Strategy",
               "x-model-default": "Fallback",
               "x-union-pattern": 1
            },
            "model_reordering": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/AutoReorder"
                  },
                  {
                     "$ref": "#/$defs/FixedOrder"
                  }
               ],
               "description": "Model reordering strategy",
               "title": "Model Reordering",
               "x-model-default": "AutoReorder",
               "x-union-pattern": 1
            },
            "optimization_solver": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/NumericalSolveSqp"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveNip"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveGlobalLocal"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveCompetedLocal"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Optimization Solver",
               "title": "Optimization Solver",
               "x-union-pattern": 2
            },
            "solver_metric": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/NumericalSolveSolverMetricAverageEstimatorVariance"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveSolverMetricNormEstimatorVariance"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveSolverMetricMaxEstimatorVariance"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Metric employed during numerical solutions in sampling-based multifidelity UQ methods.",
               "title": "Solver Metric",
               "x-union-pattern": 2
            }
         },
         "title": "NumericalSolve",
         "type": "object"
      },
      "NumericalSolveCompetedLocal": {
         "additionalProperties": false,
         "description": "Use a competed local solver scheme for solving an optimization sub-problem",
         "properties": {
            "competed_local": {
               "const": true,
               "default": true,
               "description": "Use a competed local solver scheme for solving an optimization sub-problem",
               "title": "Competed Local",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_NPSOL_OPTPP"
                  }
               ]
            }
         },
         "title": "NumericalSolveCompetedLocal",
         "type": "object"
      },
      "NumericalSolveGlobalLocal": {
         "additionalProperties": false,
         "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
         "properties": {
            "global_local": {
               "const": true,
               "default": true,
               "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
               "title": "Global Local",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_DIRECT_NPSOL_OPTPP"
                  }
               ]
            }
         },
         "title": "NumericalSolveGlobalLocal",
         "type": "object"
      },
      "NumericalSolveNip": {
         "additionalProperties": false,
         "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
         "properties": {
            "nip": {
               "const": true,
               "default": true,
               "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
               "title": "Nip",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_OPTPP"
                  }
               ]
            }
         },
         "title": "NumericalSolveNip",
         "type": "object"
      },
      "NumericalSolveSolverMetricAverageEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "average_estimator_variance": {
               "const": true,
               "default": true,
               "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
               "title": "Average Estimator Variance",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "AVG_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "title": "NumericalSolveSolverMetricAverageEstimatorVariance",
         "type": "object"
      },
      "NumericalSolveSolverMetricMaxEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "max_estimator_variance": {
               "const": true,
               "default": true,
               "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
               "title": "Max Estimator Variance",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "MAX_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "title": "NumericalSolveSolverMetricMaxEstimatorVariance",
         "type": "object"
      },
      "NumericalSolveSolverMetricNormEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "norm_estimator_variance": {
               "$ref": "#/$defs/NumericalSolveSolverMetricNormEstimatorVarianceConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NORM_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "required": [
            "norm_estimator_variance"
         ],
         "title": "NumericalSolveSolverMetricNormEstimatorVariance",
         "type": "object"
      },
      "NumericalSolveSolverMetricNormEstimatorVarianceConfig": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "norm_order": {
               "default": 2.0,
               "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.",
               "minimum": 1.0,
               "title": "Norm Order",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric_norm_order",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "title": "NumericalSolveSolverMetricNormEstimatorVarianceConfig",
         "type": "object"
      },
      "NumericalSolveSqp": {
         "additionalProperties": false,
         "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
         "properties": {
            "sqp": {
               "const": true,
               "default": true,
               "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
               "title": "Sqp",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_NPSOL"
                  }
               ]
            }
         },
         "title": "NumericalSolveSqp",
         "type": "object"
      },
      "Override": {
         "additionalProperties": false,
         "description": "Replace MFMC analytic allocation with a numerical solution",
         "properties": {
            "override": {
               "const": true,
               "default": true,
               "description": "Replace MFMC analytic allocation with a numerical solution",
               "title": "Override",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.numerical_solve_mode",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NUMERICAL_OVERRIDE"
                  }
               ]
            }
         },
         "title": "Override",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Fields:

numerical_solve (dakota.spec.shared.sampling.NumericalSolve | None)

field numerical_solve: NumericalSolve | None = None: Specify the situations where numerical optimization is used for MFMC sample allocation

pydantic model dakota.spec.shared.sampling.MlmfGroupPilotSamplesMixin

Generated model for MlmfGroupPilotSamplesMixin

Show JSON schema

{
   "title": "MlmfGroupPilotSamplesMixin",
   "description": "Generated model for MlmfGroupPilotSamplesMixin",
   "type": "object",
   "properties": {
      "pilot_samples": {
         "anyOf": [
            {
               "$ref": "#/$defs/PilotSamples"
            },
            {
               "type": "null"
            }
         ],
         "argument": "counts",
         "default": null,
         "description": "Initial set of samples for groups in the multilevel BLUE sampling method",
         "x-aliases": [
            "initial_samples"
         ]
      }
   },
   "$defs": {
      "PilotSamples": {
         "additionalProperties": false,
         "description": "Initial set of samples for groups in the multilevel BLUE sampling method",
         "properties": {
            "counts": {
               "description": "Initial set of samples for groups in the multilevel BLUE sampling method",
               "items": {
                  "type": "integer"
               },
               "title": "Counts",
               "type": "array",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.pilot_samples",
                     "ir_value_type": "SizetArray",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "independent": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Independent pilot sampling for groups in multilevel BLUE",
               "title": "Independent",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.pilot_samples.mode",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "INDEPENDENT_PILOT"
                  }
               ]
            }
         },
         "required": [
            "counts"
         ],
         "title": "PilotSamples",
         "type": "object",
         "x-model-validations": [
            {
               "validationContext": "mlmfgrouppilotsamplespilotsamples",
               "validationErrorMessage": "For mlmfgrouppilotsamplespilotsamples, all elements of counts must be >= 0.",
               "validationFields": [
                  "counts"
               ],
               "validationLiterals": [],
               "validationRuleName": "check_nonnegative_list"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

pilot_samples (dakota.spec.shared.sampling.PilotSamples | None)

field pilot_samples: PilotSamples | None = None: Initial set of samples for groups in the multilevel BLUE sampling method

pydantic model dakota.spec.shared.sampling.MlmfPilotSamplesContext1Mixin

Generated model for MlmfPilotSamplesContext1Mixin

Show JSON schema

{
   "title": "MlmfPilotSamplesContext1Mixin",
   "description": "Generated model for MlmfPilotSamplesContext1Mixin",
   "type": "object",
   "properties": {
      "pilot_samples": {
         "anyOf": [
            {
               "items": {
                  "type": "integer"
               },
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Initial set of samples for multilevel sampling methods.",
         "title": "Pilot Samples",
         "x-aliases": [
            "initial_samples"
         ],
         "x-materialization": [
            {
               "ir_key": "method.nond.pilot_samples",
               "ir_value_type": "SizetArray",
               "storage_type": "DIRECT_VALUE"
            }
         ]
      }
   },
   "additionalProperties": false,
   "x-model-validations": [
      {
         "validationContext": "mlmfpilotsamplescontext1mixin",
         "validationErrorMessage": "For mlmfpilotsamplescontext1mixin, all elements of pilot_samples must be >= 0.",
         "validationFields": [
            "pilot_samples"
         ],
         "validationLiterals": [],
         "validationRuleName": "check_nonnegative_list"
      }
   ]
}

Fields:

pilot_samples (list[int] | None)

field pilot_samples: list[int] | None = None: Initial set of samples for multilevel sampling methods.

pydantic model dakota.spec.shared.sampling.MlmfPilotSamplesContext2Mixin

Generated model for MlmfPilotSamplesContext2Mixin

Show JSON schema

{
   "title": "MlmfPilotSamplesContext2Mixin",
   "description": "Generated model for MlmfPilotSamplesContext2Mixin",
   "type": "object",
   "properties": {
      "pilot_samples": {
         "anyOf": [
            {
               "items": {
                  "type": "integer"
               },
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Initial set of samples for multilevel/multifidelity sampling methods.",
         "title": "Pilot Samples",
         "x-aliases": [
            "initial_samples"
         ],
         "x-materialization": [
            {
               "ir_key": "method.nond.pilot_samples",
               "ir_value_type": "SizetArray",
               "storage_type": "DIRECT_VALUE"
            }
         ]
      }
   },
   "additionalProperties": false,
   "x-model-validations": [
      {
         "validationContext": "mlmfpilotsamplescontext2mixin",
         "validationErrorMessage": "For mlmfpilotsamplescontext2mixin, all elements of pilot_samples must be >= 0.",
         "validationFields": [
            "pilot_samples"
         ],
         "validationLiterals": [],
         "validationRuleName": "check_nonnegative_list"
      }
   ]
}

Fields:

pilot_samples (list[int] | None)

field pilot_samples: list[int] | None = None: Initial set of samples for multilevel/multifidelity sampling methods.

pydantic model dakota.spec.shared.sampling.NumericalSolve

Specify the situations where numerical optimization is used for MFMC sample allocation

Show JSON schema

{
   "title": "NumericalSolve",
   "description": "Specify the situations where numerical optimization is used for MFMC sample allocation",
   "type": "object",
   "properties": {
      "numerical_solve_strategy": {
         "anchor": true,
         "anyOf": [
            {
               "$ref": "#/$defs/Fallback"
            },
            {
               "$ref": "#/$defs/Override"
            }
         ],
         "description": "Employ numerical solve",
         "title": "Numerical Solve Strategy",
         "x-model-default": "Fallback",
         "x-union-pattern": 1
      },
      "model_reordering": {
         "anyOf": [
            {
               "$ref": "#/$defs/AutoReorder"
            },
            {
               "$ref": "#/$defs/FixedOrder"
            }
         ],
         "description": "Model reordering strategy",
         "title": "Model Reordering",
         "x-model-default": "AutoReorder",
         "x-union-pattern": 1
      },
      "optimization_solver": {
         "anchor": true,
         "anyOf": [
            {
               "$ref": "#/$defs/NumericalSolveSqp"
            },
            {
               "$ref": "#/$defs/NumericalSolveNip"
            },
            {
               "$ref": "#/$defs/NumericalSolveGlobalLocal"
            },
            {
               "$ref": "#/$defs/NumericalSolveCompetedLocal"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Optimization Solver",
         "title": "Optimization Solver",
         "x-union-pattern": 2
      },
      "solver_metric": {
         "anyOf": [
            {
               "$ref": "#/$defs/NumericalSolveSolverMetricAverageEstimatorVariance"
            },
            {
               "$ref": "#/$defs/NumericalSolveSolverMetricNormEstimatorVariance"
            },
            {
               "$ref": "#/$defs/NumericalSolveSolverMetricMaxEstimatorVariance"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Metric employed during numerical solutions in sampling-based multifidelity UQ methods.",
         "title": "Solver Metric",
         "x-union-pattern": 2
      }
   },
   "$defs": {
      "AutoReorder": {
         "additionalProperties": false,
         "description": "Reorder models automatically",
         "properties": {
            "auto_reorder": {
               "const": true,
               "default": true,
               "description": "Reorder models automatically",
               "title": "Auto Reorder",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.model_reordering",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "REORDER_MODELS_ON_THE_FLY"
                  }
               ]
            }
         },
         "title": "AutoReorder",
         "type": "object"
      },
      "Fallback": {
         "additionalProperties": false,
         "description": "Fall back to a numerical solve when needed for mitigation in MFMC",
         "properties": {
            "fallback": {
               "const": true,
               "default": true,
               "description": "Fall back to a numerical solve when needed for mitigation in MFMC",
               "title": "Fallback",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.numerical_solve_mode",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NUMERICAL_FALLBACK"
                  }
               ]
            }
         },
         "title": "Fallback",
         "type": "object"
      },
      "FixedOrder": {
         "additionalProperties": false,
         "description": "Used a fixed model order",
         "properties": {
            "fixed_order": {
               "const": true,
               "default": true,
               "description": "Used a fixed model order",
               "title": "Fixed Order",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.model_reordering",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "FIXED_MODEL_ORDERING"
                  }
               ]
            }
         },
         "title": "FixedOrder",
         "type": "object"
      },
      "NumericalSolveCompetedLocal": {
         "additionalProperties": false,
         "description": "Use a competed local solver scheme for solving an optimization sub-problem",
         "properties": {
            "competed_local": {
               "const": true,
               "default": true,
               "description": "Use a competed local solver scheme for solving an optimization sub-problem",
               "title": "Competed Local",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_NPSOL_OPTPP"
                  }
               ]
            }
         },
         "title": "NumericalSolveCompetedLocal",
         "type": "object"
      },
      "NumericalSolveGlobalLocal": {
         "additionalProperties": false,
         "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
         "properties": {
            "global_local": {
               "const": true,
               "default": true,
               "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
               "title": "Global Local",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_DIRECT_NPSOL_OPTPP"
                  }
               ]
            }
         },
         "title": "NumericalSolveGlobalLocal",
         "type": "object"
      },
      "NumericalSolveNip": {
         "additionalProperties": false,
         "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
         "properties": {
            "nip": {
               "const": true,
               "default": true,
               "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
               "title": "Nip",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_OPTPP"
                  }
               ]
            }
         },
         "title": "NumericalSolveNip",
         "type": "object"
      },
      "NumericalSolveSolverMetricAverageEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "average_estimator_variance": {
               "const": true,
               "default": true,
               "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
               "title": "Average Estimator Variance",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "AVG_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "title": "NumericalSolveSolverMetricAverageEstimatorVariance",
         "type": "object"
      },
      "NumericalSolveSolverMetricMaxEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "max_estimator_variance": {
               "const": true,
               "default": true,
               "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
               "title": "Max Estimator Variance",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "MAX_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "title": "NumericalSolveSolverMetricMaxEstimatorVariance",
         "type": "object"
      },
      "NumericalSolveSolverMetricNormEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "norm_estimator_variance": {
               "$ref": "#/$defs/NumericalSolveSolverMetricNormEstimatorVarianceConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NORM_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "required": [
            "norm_estimator_variance"
         ],
         "title": "NumericalSolveSolverMetricNormEstimatorVariance",
         "type": "object"
      },
      "NumericalSolveSolverMetricNormEstimatorVarianceConfig": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "norm_order": {
               "default": 2.0,
               "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.",
               "minimum": 1.0,
               "title": "Norm Order",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric_norm_order",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "title": "NumericalSolveSolverMetricNormEstimatorVarianceConfig",
         "type": "object"
      },
      "NumericalSolveSqp": {
         "additionalProperties": false,
         "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
         "properties": {
            "sqp": {
               "const": true,
               "default": true,
               "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
               "title": "Sqp",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_NPSOL"
                  }
               ]
            }
         },
         "title": "NumericalSolveSqp",
         "type": "object"
      },
      "Override": {
         "additionalProperties": false,
         "description": "Replace MFMC analytic allocation with a numerical solution",
         "properties": {
            "override": {
               "const": true,
               "default": true,
               "description": "Replace MFMC analytic allocation with a numerical solution",
               "title": "Override",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.numerical_solve_mode",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NUMERICAL_OVERRIDE"
                  }
               ]
            }
         },
         "title": "Override",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Fields:

model_reordering (dakota.spec.shared.sampling.AutoReorder | dakota.spec.shared.sampling.FixedOrder)
numerical_solve_strategy (dakota.spec.shared.sampling.Fallback | dakota.spec.shared.sampling.Override)
optimization_solver (dakota.spec.shared.sampling.NumericalSolveSqp | dakota.spec.shared.sampling.NumericalSolveNip | dakota.spec.shared.sampling.NumericalSolveGlobalLocal | dakota.spec.shared.sampling.NumericalSolveCompetedLocal | None)
solver_metric (dakota.spec.shared.sampling.NumericalSolveSolverMetricAverageEstimatorVariance | dakota.spec.shared.sampling.NumericalSolveSolverMetricNormEstimatorVariance | dakota.spec.shared.sampling.NumericalSolveSolverMetricMaxEstimatorVariance | None)

field model_reordering: AutoReorder | FixedOrder [Optional]: Model reordering strategy

field numerical_solve_strategy: Fallback | Override [Optional]: Employ numerical solve

field optimization_solver: NumericalSolveSqp | NumericalSolveNip | NumericalSolveGlobalLocal | NumericalSolveCompetedLocal | None = None: Optimization Solver

field solver_metric: NumericalSolveSolverMetricAverageEstimatorVariance | NumericalSolveSolverMetricNormEstimatorVariance | NumericalSolveSolverMetricMaxEstimatorVariance | None = None: Metric employed during numerical solutions in sampling-based multifidelity UQ methods.

pydantic model dakota.spec.shared.sampling.NumericalSolveCompetedLocal

Use a competed local solver scheme for solving an optimization sub-problem

Show JSON schema

{
   "title": "NumericalSolveCompetedLocal",
   "description": "Use a competed local solver scheme for solving an optimization sub-problem",
   "type": "object",
   "properties": {
      "competed_local": {
         "const": true,
         "default": true,
         "description": "Use a competed local solver scheme for solving an optimization sub-problem",
         "title": "Competed Local",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.opt_subproblem_solver",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "SUBMETHOD_NPSOL_OPTPP"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

competed_local (Literal[True])

field competed_local: Literal[True] = True: Use a competed local solver scheme for solving an optimization sub-problem

pydantic model dakota.spec.shared.sampling.NumericalSolveGlobalLocal

Use a hybrid global-local scheme for solving an optimization sub-problem

Show JSON schema

{
   "title": "NumericalSolveGlobalLocal",
   "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
   "type": "object",
   "properties": {
      "global_local": {
         "const": true,
         "default": true,
         "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
         "title": "Global Local",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.opt_subproblem_solver",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "SUBMETHOD_DIRECT_NPSOL_OPTPP"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

global_local (Literal[True])

field global_local: Literal[True] = True: Use a hybrid global-local scheme for solving an optimization sub-problem

pydantic model dakota.spec.shared.sampling.NumericalSolveNip

Use a nonlinear interior point method for solving an optimization sub-problem

Show JSON schema

{
   "title": "NumericalSolveNip",
   "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
   "type": "object",
   "properties": {
      "nip": {
         "const": true,
         "default": true,
         "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
         "title": "Nip",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.opt_subproblem_solver",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "SUBMETHOD_OPTPP"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

nip (Literal[True])

field nip: Literal[True] = True: Use a nonlinear interior point method for solving an optimization sub-problem

pydantic model dakota.spec.shared.sampling.NumericalSolveSolverMetricAverageEstimatorVariance

Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.

Show JSON schema

{
   "title": "NumericalSolveSolverMetricAverageEstimatorVariance",
   "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
   "type": "object",
   "properties": {
      "average_estimator_variance": {
         "const": true,
         "default": true,
         "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
         "title": "Average Estimator Variance",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.estimator_variance_metric",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "AVG_ESTVAR_METRIC"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

average_estimator_variance (Literal[True])

field average_estimator_variance: Literal[True] = True: Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.

pydantic model dakota.spec.shared.sampling.NumericalSolveSolverMetricMaxEstimatorVariance

Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.

Show JSON schema

{
   "title": "NumericalSolveSolverMetricMaxEstimatorVariance",
   "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
   "type": "object",
   "properties": {
      "max_estimator_variance": {
         "const": true,
         "default": true,
         "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
         "title": "Max Estimator Variance",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.estimator_variance_metric",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "MAX_ESTVAR_METRIC"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

max_estimator_variance (Literal[True])

field max_estimator_variance: Literal[True] = True: Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.

pydantic model dakota.spec.shared.sampling.NumericalSolveSolverMetricNormEstimatorVariance

Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.

Show JSON schema

{
   "title": "NumericalSolveSolverMetricNormEstimatorVariance",
   "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
   "type": "object",
   "properties": {
      "norm_estimator_variance": {
         "$ref": "#/$defs/NumericalSolveSolverMetricNormEstimatorVarianceConfig",
         "x-materialization": [
            {
               "ir_key": "method.nond.estimator_variance_metric",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "NORM_ESTVAR_METRIC"
            }
         ]
      }
   },
   "$defs": {
      "NumericalSolveSolverMetricNormEstimatorVarianceConfig": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "norm_order": {
               "default": 2.0,
               "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.",
               "minimum": 1.0,
               "title": "Norm Order",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric_norm_order",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "title": "NumericalSolveSolverMetricNormEstimatorVarianceConfig",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "required": [
      "norm_estimator_variance"
   ]
}

Fields:

norm_estimator_variance (dakota.spec.shared.sampling.NumericalSolveSolverMetricNormEstimatorVarianceConfig)

field norm_estimator_variance: NumericalSolveSolverMetricNormEstimatorVarianceConfig [Required]: Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.

pydantic model dakota.spec.shared.sampling.NumericalSolveSolverMetricNormEstimatorVarianceConfig

Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.

Show JSON schema

{
   "title": "NumericalSolveSolverMetricNormEstimatorVarianceConfig",
   "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
   "type": "object",
   "properties": {
      "norm_order": {
         "default": 2.0,
         "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.",
         "minimum": 1.0,
         "title": "Norm Order",
         "type": "number",
         "x-materialization": [
            {
               "ir_key": "method.nond.estimator_variance_metric_norm_order",
               "ir_value_type": "Real",
               "storage_type": "DIRECT_VALUE"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

norm_order (float)

field norm_order: DakotaFloat = 2.0

Utilize the response covariance metric for guiding adaptive refinement during UQ.

Constraints:

ge = 1.0
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json

pydantic model dakota.spec.shared.sampling.NumericalSolveSqp

Use a sequential quadratic programming method for solving an optimization sub-problem

Show JSON schema

{
   "title": "NumericalSolveSqp",
   "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
   "type": "object",
   "properties": {
      "sqp": {
         "const": true,
         "default": true,
         "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
         "title": "Sqp",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.opt_subproblem_solver",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "SUBMETHOD_NPSOL"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

sqp (Literal[True])

field sqp: Literal[True] = True: Use a sequential quadratic programming method for solving an optimization sub-problem

pydantic model dakota.spec.shared.sampling.Override

Replace MFMC analytic allocation with a numerical solution

Show JSON schema

{
   "title": "Override",
   "description": "Replace MFMC analytic allocation with a numerical solution",
   "type": "object",
   "properties": {
      "override": {
         "const": true,
         "default": true,
         "description": "Replace MFMC analytic allocation with a numerical solution",
         "title": "Override",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.numerical_solve_mode",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "NUMERICAL_OVERRIDE"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

override (Literal[True])

field override: Literal[True] = True: Replace MFMC analytic allocation with a numerical solution

pydantic model dakota.spec.shared.sampling.PilotSamples

Initial set of samples for groups in the multilevel BLUE sampling method

Show JSON schema

{
   "title": "PilotSamples",
   "description": "Initial set of samples for groups in the multilevel BLUE sampling method",
   "type": "object",
   "properties": {
      "counts": {
         "description": "Initial set of samples for groups in the multilevel BLUE sampling method",
         "items": {
            "type": "integer"
         },
         "title": "Counts",
         "type": "array",
         "x-materialization": [
            {
               "ir_key": "method.nond.pilot_samples",
               "ir_value_type": "SizetArray",
               "storage_type": "DIRECT_VALUE"
            }
         ]
      },
      "independent": {
         "anyOf": [
            {
               "const": true,
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Independent pilot sampling for groups in multilevel BLUE",
         "title": "Independent",
         "x-materialization": [
            {
               "ir_key": "method.nond.pilot_samples.mode",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "INDEPENDENT_PILOT"
            }
         ]
      }
   },
   "additionalProperties": false,
   "required": [
      "counts"
   ],
   "x-model-validations": [
      {
         "validationContext": "mlmfgrouppilotsamplespilotsamples",
         "validationErrorMessage": "For mlmfgrouppilotsamplespilotsamples, all elements of counts must be >= 0.",
         "validationFields": [
            "counts"
         ],
         "validationLiterals": [],
         "validationRuleName": "check_nonnegative_list"
      }
   ]
}

Fields:

counts (list[int])
independent (Literal[True] | None)

field counts: list[int] [Required]: Initial set of samples for groups in the multilevel BLUE sampling method

field independent: Literal[True] | None = None: Independent pilot sampling for groups in multilevel BLUE

pydantic model dakota.spec.shared.sampling.SamplesOnEmulatorWithAliasMixin

Generated model for SamplesOnEmulatorWithAliasMixin

Show JSON schema

{
   "title": "SamplesOnEmulatorWithAliasMixin",
   "description": "Generated model for SamplesOnEmulatorWithAliasMixin",
   "type": "object",
   "properties": {
      "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"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

samples_on_emulator (int)

field samples_on_emulator: int = 0: Number of samples at which to evaluate an emulator (surrogate)

Sampling

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