multifidelity_sampling

pydantic model dakota.spec.method.multifidelity_sampling.MultifidelitySamplingSelection

Generated model for MultifidelitySamplingSelection

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{
   "title": "MultifidelitySamplingSelection",
   "description": "Generated model for MultifidelitySamplingSelection",
   "type": "object",
   "properties": {
      "multifidelity_sampling": {
         "$ref": "#/$defs/MultifidelitySamplingConfig",
         "x-aliases": [
            "multifidelity_mc",
            "mfmc"
         ],
         "x-materialization": [
            {
               "ir_key": "method.algorithm",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "MULTIFIDELITY_SAMPLING"
            }
         ]
      }
   },
   "$defs": {
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         "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"
      },
      "Debug": {
         "additionalProperties": false,
         "description": "Level 5 of 5 - maximum",
         "properties": {
            "debug": {
               "const": true,
               "default": true,
               "description": "Level 5 of 5 - maximum",
               "title": "Debug",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
                     "ir_value_type": "short",
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                     "stored_value": "DEBUG_OUTPUT"
                  }
               ]
            }
         },
         "title": "Debug",
         "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",
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                     "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"
      },
      "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",
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                  }
               ]
            }
         },
         "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": [
                  {
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                     "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",
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                  {
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                  }
               ]
            }
         },
         "title": "MethodConvergenceTolWithTypeContext3Relative",
         "type": "object"
      },
      "MethodExportSamplesFormatAnnotated": {
         "additionalProperties": false,
         "description": "Selects annotated tabular file format",
         "properties": {
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               "const": true,
               "default": true,
               "description": "Selects annotated tabular file format",
               "title": "Annotated",
               "type": "boolean",
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                  {
                     "ir_key": "method.nond.export_samples_format",
                     "ir_value_type": "unsigned short",
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               ]
            }
         },
         "title": "MethodExportSamplesFormatAnnotated",
         "type": "object"
      },
      "MethodExportSamplesFormatCustomAnnotated": {
         "additionalProperties": false,
         "description": "Selects custom-annotated tabular file format",
         "properties": {
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               ],
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            }
         },
         "title": "MethodExportSamplesFormatCustomAnnotated",
         "type": "object"
      },
      "MethodExportSamplesFormatCustomAnnotatedConfig": {
         "additionalProperties": false,
         "description": "Selects custom-annotated tabular file format",
         "properties": {
            "header": {
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                  {
                     "const": true,
                     "type": "boolean"
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                  {
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               ],
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                     "storage_type": "AUGMENT_ENUM",
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               ]
            },
            "eval_id": {
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                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Enable evaluation ID column in custom-annotated tabular file",
               "title": "Eval Id",
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                     "ir_value_type": "unsigned short",
                     "storage_type": "AUGMENT_ENUM",
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               ]
            },
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                     "const": true,
                     "type": "boolean"
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                     "type": "null"
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               "title": "Interface Id",
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                  {
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                     "ir_value_type": "unsigned short",
                     "storage_type": "AUGMENT_ENUM",
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                  }
               ]
            }
         },
         "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",
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                  }
               ]
            }
         },
         "title": "MethodExportSamplesFormatFreeform",
         "type": "object"
      },
      "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",
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                  }
               ]
            }
         },
         "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"
      },
      "MultifidelitySamplingConfig": {
         "additionalProperties": false,
         "description": "Multifidelity Monte Carlo sampling method for UQ",
         "properties": {
            "model_pointer": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Identifier for model block to be used by a method",
               "title": "Model Pointer",
               "x-block-pointer": "model",
               "x-materialization": [
                  {
                     "ir_key": "method.model_pointer",
                     "ir_value_type": "String",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "rng": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/RngOptionsContext2Mt19937"
                  },
                  {
                     "$ref": "#/$defs/RngOptionsContext2Rnum2"
                  }
               ],
               "description": "Selection of a random number generator",
               "title": "Rng",
               "x-model-default": "RngOptionsContext2Mt19937",
               "x-union-pattern": 1
            },
            "max_function_evaluations": {
               "default": 9223372036854775807,
               "description": "Stopping criterion based on maximum function evaluations",
               "minimum": 0,
               "title": "Max Function Evaluations",
               "type": "integer",
               "x-materialization": [
                  {
                     "ir_key": "method.max_function_evaluations",
                     "ir_value_type": "size_t",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "max_iterations": {
               "default": 9223372036854775807,
               "description": "Number of iterations allowed for optimizers and adaptive UQ methods",
               "minimum": 0,
               "title": "Max Iterations",
               "type": "integer",
               "x-materialization": [
                  {
                     "ir_key": "method.max_iterations",
                     "ir_value_type": "size_t",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "convergence_tolerance": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodConvergenceTolWithTypeContext3ConvergenceTol"
                  },
                  {
                     "type": "null"
                  }
               ],
               "argument": "value",
               "default": null,
               "description": "Stopping criterion based on relative error reduction"
            },
            "sample_type": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodSampleTypeLhsMcLhs"
                  },
                  {
                     "$ref": "#/$defs/MethodSampleTypeLhsMcRandom"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Selection of sampling strategy",
               "title": "Sample Type",
               "x-union-pattern": 2
            },
            "search_model_graphs": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/PromotedModelSelectionContext1SearchModelGraphs"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Perform a search over admissible model relationships for a given model ensemble"
            },
            "numerical_solve": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/NumericalSolve"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Specify the situations where numerical optimization is used for MFMC sample allocation"
            },
            "solution_mode": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/OnlinePilot"
                  },
                  {
                     "$ref": "#/$defs/OfflinePilot"
                  },
                  {
                     "$ref": "#/$defs/OnlineProjection"
                  },
                  {
                     "$ref": "#/$defs/OfflineProjection"
                  }
               ],
               "description": "Solution mode for multilevel/multifidelity methods",
               "title": "Solution Mode",
               "x-model-default": "OnlinePilot",
               "x-union-pattern": 1
            },
            "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"
                  }
               ]
            },
            "seed_sequence": {
               "anyOf": [
                  {
                     "items": {
                        "type": "integer"
                     },
                     "type": "array"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Sequence of seed values for multi-stage random sampling",
               "title": "Seed Sequence",
               "x-materialization": [
                  {
                     "ir_key": "method.random_seed_sequence",
                     "ir_value_type": "SizetArray",
                     "storage_type": "DIRECT_VALUE"
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               ]
            },
            "fixed_seed": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Reuses the same seed value for multiple random sampling sets",
               "title": "Fixed Seed",
               "x-materialization": [
                  {
                     "ir_key": "method.fixed_seed",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            },
            "id_method": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Name the method block; helpful when there are multiple",
               "title": "Id Method",
               "x-materialization": [
                  {
                     "ir_key": "method.id",
                     "ir_value_type": "String",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "output": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/Debug"
                  },
                  {
                     "$ref": "#/$defs/Verbose"
                  },
                  {
                     "$ref": "#/$defs/Normal"
                  },
                  {
                     "$ref": "#/$defs/Quiet"
                  },
                  {
                     "$ref": "#/$defs/Silent"
                  }
               ],
               "description": "Control how much method information is written to the screen and output file",
               "title": "Output",
               "x-model-default": "Normal",
               "x-union-pattern": 1
            },
            "final_solutions": {
               "default": 0,
               "description": "Number of designs returned as the best solutions",
               "minimum": 0,
               "title": "Final Solutions",
               "type": "integer",
               "x-materialization": [
                  {
                     "ir_key": "method.final_solutions",
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                     "storage_type": "DIRECT_VALUE"
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               ]
            },
            "export_sample_sequence": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/MultifidelitySamplingExportSampleSequence"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Enable export of multilevel/multifidelity sample sequences to individual files",
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                  {
                     "ir_key": "method.nond.export_sample_sequence",
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                     "storage_type": "PRESENCE_TRUE"
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               ]
            }
         },
         "title": "MultifidelitySamplingConfig",
         "type": "object",
         "x-model-validations": [
            {
               "validationContext": "mlmfpilotsamplescontext2mixin",
               "validationErrorMessage": "For mlmfpilotsamplescontext2mixin, all elements of pilot_samples must be >= 0.",
               "validationFields": [
                  "pilot_samples"
               ],
               "validationLiterals": [],
               "validationRuleName": "check_nonnegative_list"
            }
         ]
      },
      "MultifidelitySamplingExportSampleSequence": {
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         "description": "Enable export of multilevel/multifidelity sample sequences to individual files",
         "properties": {
            "format": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodExportSamplesFormatCustomAnnotated"
                  },
                  {
                     "$ref": "#/$defs/MethodExportSamplesFormatAnnotated"
                  },
                  {
                     "$ref": "#/$defs/MethodExportSamplesFormatFreeform"
                  }
               ],
               "description": "Tabular Format",
               "title": "Format",
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               "x-union-pattern": 1
            }
         },
         "title": "MultifidelitySamplingExportSampleSequence",
         "type": "object"
      },
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         "additionalProperties": false,
         "description": "Level 3 of 5 - default",
         "properties": {
            "normal": {
               "const": true,
               "default": true,
               "description": "Level 3 of 5 - default",
               "title": "Normal",
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               ]
            }
         },
         "title": "Normal",
         "type": "object"
      },
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         "description": "Specify the situations where numerical optimization is used for MFMC sample allocation",
         "properties": {
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               "anchor": true,
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                  {
                     "$ref": "#/$defs/Fallback"
                  },
                  {
                     "$ref": "#/$defs/Override"
                  }
               ],
               "description": "Employ numerical solve",
               "title": "Numerical Solve Strategy",
               "x-model-default": "Fallback",
               "x-union-pattern": 1
            },
            "model_reordering": {
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                  {
                     "$ref": "#/$defs/AutoReorder"
                  },
                  {
                     "$ref": "#/$defs/FixedOrder"
                  }
               ],
               "description": "Model reordering strategy",
               "title": "Model Reordering",
               "x-model-default": "AutoReorder",
               "x-union-pattern": 1
            },
            "optimization_solver": {
               "anchor": true,
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                  {
                     "$ref": "#/$defs/NumericalSolveSqp"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveNip"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveGlobalLocal"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveCompetedLocal"
                  },
                  {
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                  }
               ],
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               "x-union-pattern": 2
            },
            "solver_metric": {
               "anyOf": [
                  {
                     "$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": {
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         "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",
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                  {
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                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_NPSOL_OPTPP"
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               ]
            }
         },
         "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",
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                  {
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                     "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,
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         },
         "title": "NumericalSolveNip",
         "type": "object"
      },
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         "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,
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               "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
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         },
         "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,
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               "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
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         "title": "NumericalSolveSolverMetricMaxEstimatorVariance",
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      "NumericalSolveSolverMetricNormEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
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      "NumericalSolveSolverMetricNormEstimatorVarianceConfig": {
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         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
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         "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
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         "additionalProperties": false,
         "description": "Specify a solution mode that excludes the pilot cost from sample allocation logic",
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               "$ref": "#/$defs/OfflinePilotConfig",
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         "additionalProperties": false,
         "description": "Specify a solution mode that excludes the pilot cost from sample allocation logic",
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         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
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         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard"
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsDistributionComplementary"
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         "description": "Specify a solution mode that estimates performance based on projecting initial correlation/variance estimates from an offline pilot sample",
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               "description": "Specify a solution mode that estimates performance based on projecting initial correlation/variance estimates from an offline pilot sample",
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         "title": "OfflineProjection",
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         "additionalProperties": false,
         "description": "Specify a solution mode that includes the pilot cost within the sample allocation logic",
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               "$ref": "#/$defs/OnlinePilotConfig",
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         "title": "OnlinePilot",
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         "description": "Specify a solution mode that includes the pilot cost within the sample allocation logic",
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               "anyOf": [
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                     "$ref": "#/$defs/OnlinePilotRelaxationFactorSequence"
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                  {
                     "$ref": "#/$defs/OnlinePilotRelaxationFixedFactor"
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                     "$ref": "#/$defs/OnlinePilotRelaxationRecursiveFactor"
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatistics"
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                  {
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               ],
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               "description": "Indicate the type of final statistics to be returned by a UQ method",
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         },
         "title": "OnlinePilotConfig",
         "type": "object"
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      "OnlinePilotFinalStatisticsEstimatorPerformance": {
         "additionalProperties": false,
         "description": "Return estimator performance as the final results of a UQ method",
         "properties": {
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               "description": "Return estimator performance as the final results of a UQ method",
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               ]
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         "title": "OnlinePilotFinalStatisticsEstimatorPerformance",
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      "OnlinePilotFinalStatisticsQoiStatistics": {
         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
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         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
         "properties": {
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsNone"
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                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard"
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsCentral"
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsDistributionComplementary"
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               ],
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         "description": "Placeholder for future capabilities",
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      "OnlinePilotFinalStatisticsQoiStatisticsDistributionCumulative": {
         "additionalProperties": false,
         "description": "Placeholder for future capabilities",
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               "description": "Placeholder for future capabilities",
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         "description": "Output central moments and include them within the set of final statistics.",
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               "description": "Output central moments and include them within the set of final statistics.",
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         "additionalProperties": false,
         "description": "Omit moments from the set of final statistics.",
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               "description": "Omit moments from the set of final statistics.",
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               "type": "boolean",
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      "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard": {
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         "description": "Output standardized moments and include them within the set of final statistics.",
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         "additionalProperties": false,
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         "additionalProperties": false,
         "description": "Specify a solution mode that estimates performance based on projecting initial correlation / covariance estimates from an online pilot sample",
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               "description": "Specify a solution mode that estimates performance based on projecting initial correlation / covariance estimates from an online pilot sample",
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         },
         "title": "OnlineProjection",
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         "description": "Replace MFMC analytic allocation with a numerical solution",
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               "description": "Replace MFMC analytic allocation with a numerical solution",
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         "title": "Override",
         "type": "object"
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         "title": "PromotedModelSelectionContext1SearchModelGraphsFullRecursion",
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               "description": "Do not recur over admissible DAGs for a given model ensemble",
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         },
         "title": "PromotedModelSelectionContext1SearchModelGraphsNoRecursion",
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      "Quiet": {
         "additionalProperties": false,
         "description": "Level 2 of 5 - less than normal",
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               "const": true,
               "default": true,
               "description": "Level 2 of 5 - less than normal",
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               ]
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         },
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                     "storage_type": "PRESENCE_LITERAL",
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                  }
               ]
            }
         },
         "title": "RngOptionsContext2Mt19937",
         "type": "object"
      },
      "RngOptionsContext2Rnum2": {
         "additionalProperties": false,
         "description": "Generates pseudo-random numbers using the Pecos package",
         "properties": {
            "rnum2": {
               "const": true,
               "default": true,
               "description": "Generates pseudo-random numbers using the Pecos package",
               "title": "Rnum2",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.random_number_generator",
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                     "storage_type": "PRESENCE_LITERAL",
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                  }
               ]
            }
         },
         "title": "RngOptionsContext2Rnum2",
         "type": "object"
      },
      "Silent": {
         "additionalProperties": false,
         "description": "Level 1 of 5 - minimum",
         "properties": {
            "silent": {
               "const": true,
               "default": true,
               "description": "Level 1 of 5 - minimum",
               "title": "Silent",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
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               ]
            }
         },
         "title": "Silent",
         "type": "object"
      },
      "Verbose": {
         "additionalProperties": false,
         "description": "Level 4 of 5 - more than normal",
         "properties": {
            "verbose": {
               "const": true,
               "default": true,
               "description": "Level 4 of 5 - more than normal",
               "title": "Verbose",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
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                  }
               ]
            }
         },
         "title": "Verbose",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "required": [
      "multifidelity_sampling"
   ]
}

Fields:

• multifidelity_sampling (dakota.spec.method.multifidelity_sampling.MultifidelitySamplingConfig)

field multifidelity_sampling: MultifidelitySamplingConfig [Required]

classmethod get_registry() → dict[str, type[MethodSelection]]

Get registry, performing deferred registration on first call

classmethod get_union()

Generate Union from all registered selections

pydantic model dakota.spec.method.multifidelity_sampling.MultifidelitySamplingConfig

Multifidelity Monte Carlo sampling method for UQ

Show JSON schema

{
   "title": "MultifidelitySamplingConfig",
   "description": "Multifidelity Monte Carlo sampling method for UQ",
   "type": "object",
   "properties": {
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            {
               "type": "string"
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         "title": "Model Pointer",
         "x-block-pointer": "model",
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            {
               "ir_key": "method.model_pointer",
               "ir_value_type": "String",
               "storage_type": "DIRECT_VALUE"
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      },
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            },
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         ],
         "description": "Selection of a random number generator",
         "title": "Rng",
         "x-model-default": "RngOptionsContext2Mt19937",
         "x-union-pattern": 1
      },
      "max_function_evaluations": {
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         "minimum": 0,
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      },
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         "description": "Number of iterations allowed for optimizers and adaptive UQ methods",
         "minimum": 0,
         "title": "Max Iterations",
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         ]
      },
      "convergence_tolerance": {
         "anyOf": [
            {
               "$ref": "#/$defs/MethodConvergenceTolWithTypeContext3ConvergenceTol"
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            {
               "type": "null"
            }
         ],
         "argument": "value",
         "default": null,
         "description": "Stopping criterion based on relative error reduction"
      },
      "sample_type": {
         "anyOf": [
            {
               "$ref": "#/$defs/MethodSampleTypeLhsMcLhs"
            },
            {
               "$ref": "#/$defs/MethodSampleTypeLhsMcRandom"
            },
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               "type": "null"
            }
         ],
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         "title": "Sample Type",
         "x-union-pattern": 2
      },
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         "anyOf": [
            {
               "$ref": "#/$defs/PromotedModelSelectionContext1SearchModelGraphs"
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               "type": "null"
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         ],
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               "$ref": "#/$defs/OfflineProjection"
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         "description": "Solution mode for multilevel/multifidelity methods",
         "title": "Solution Mode",
         "x-model-default": "OnlinePilot",
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      },
      "pilot_samples": {
         "anyOf": [
            {
               "items": {
                  "type": "integer"
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               "type": "null"
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         ],
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         "description": "Initial set of samples for multilevel/multifidelity sampling methods.",
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         "x-aliases": [
            "initial_samples"
         ],
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               "ir_key": "method.nond.pilot_samples",
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         ]
      },
      "seed_sequence": {
         "anyOf": [
            {
               "items": {
                  "type": "integer"
               },
               "type": "array"
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            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Sequence of seed values for multi-stage random sampling",
         "title": "Seed Sequence",
         "x-materialization": [
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               "ir_key": "method.random_seed_sequence",
               "ir_value_type": "SizetArray",
               "storage_type": "DIRECT_VALUE"
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         ]
      },
      "fixed_seed": {
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            {
               "const": true,
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            {
               "type": "null"
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         ],
         "default": null,
         "description": "Reuses the same seed value for multiple random sampling sets",
         "title": "Fixed Seed",
         "x-materialization": [
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         ]
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               "type": "string"
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            {
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         ],
         "default": null,
         "description": "Name the method block; helpful when there are multiple",
         "title": "Id Method",
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               "storage_type": "DIRECT_VALUE"
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         ]
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            },
            {
               "$ref": "#/$defs/Quiet"
            },
            {
               "$ref": "#/$defs/Silent"
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         ],
         "description": "Control how much method information is written to the screen and output file",
         "title": "Output",
         "x-model-default": "Normal",
         "x-union-pattern": 1
      },
      "final_solutions": {
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         "description": "Number of designs returned as the best solutions",
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         "title": "Final Solutions",
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            {
               "$ref": "#/$defs/MultifidelitySamplingExportSampleSequence"
            },
            {
               "type": "null"
            }
         ],
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   },
   "$defs": {
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         "description": "Reorder models automatically",
         "properties": {
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               "const": true,
               "default": true,
               "description": "Reorder models automatically",
               "title": "Auto Reorder",
               "type": "boolean",
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               ]
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         },
         "title": "AutoReorder",
         "type": "object"
      },
      "Debug": {
         "additionalProperties": false,
         "description": "Level 5 of 5 - maximum",
         "properties": {
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               "const": true,
               "default": true,
               "description": "Level 5 of 5 - maximum",
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               "type": "boolean",
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               ]
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         },
         "title": "Debug",
         "type": "object"
      },
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         "description": "Fall back to a numerical solve when needed for mitigation in MFMC",
         "properties": {
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               "const": true,
               "default": true,
               "description": "Fall back to a numerical solve when needed for mitigation in MFMC",
               "title": "Fallback",
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               "x-materialization": [
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                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NUMERICAL_FALLBACK"
                  }
               ]
            }
         },
         "title": "Fallback",
         "type": "object"
      },
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         "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",
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                  {
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                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "FIXED_MODEL_ORDERING"
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               ]
            }
         },
         "title": "FixedOrder",
         "type": "object"
      },
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         "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods",
         "properties": {
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               "default": true,
               "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods",
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         "type": "object"
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         "description": "Stopping criterion based on relative error reduction",
         "properties": {
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               "default": -1.7976931348623157e+308,
               "description": "Stopping criterion based on relative error reduction",
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                     "ir_key": "method.jega.percent_change",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
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               ]
            },
            "convergence_tolerance_type": {
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               "anyOf": [
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                     "$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",
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                     "storage_type": "PRESENCE_ENUM",
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               ]
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         },
         "title": "MethodConvergenceTolWithTypeContext3Relative",
         "type": "object"
      },
      "MethodExportSamplesFormatAnnotated": {
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         "description": "Selects annotated tabular file format",
         "properties": {
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               "default": true,
               "description": "Selects annotated tabular file format",
               "title": "Annotated",
               "type": "boolean",
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               ],
               "x-model-default": "MethodExportSamplesFormatCustomAnnotatedConfig"
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         },
         "title": "MethodExportSamplesFormatCustomAnnotated",
         "type": "object"
      },
      "MethodExportSamplesFormatCustomAnnotatedConfig": {
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         "description": "Selects custom-annotated tabular file format",
         "properties": {
            "header": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
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               ],
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                     "storage_type": "AUGMENT_ENUM",
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         "properties": {
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               ]
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         },
         "title": "MethodExportSamplesFormatFreeform",
         "type": "object"
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      "MethodSampleTypeLhsMcLhs": {
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         "description": "Uses Latin Hypercube Sampling (LHS) to sample variables",
         "properties": {
            "lhs": {
               "const": true,
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         },
         "title": "MultifidelitySamplingExportSampleSequence",
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         "properties": {
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         },
         "title": "Normal",
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      "NumericalSolve": {
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               "title": "Model Reordering",
               "x-model-default": "AutoReorder",
               "x-union-pattern": 1
            },
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                  },
                  {
                     "$ref": "#/$defs/NumericalSolveNip"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveGlobalLocal"
                  },
                  {
                     "$ref": "#/$defs/NumericalSolveCompetedLocal"
                  },
                  {
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                  }
               ],
               "default": null,
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               "x-union-pattern": 2
            },
            "solver_metric": {
               "anyOf": [
                  {
                     "$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",
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               ]
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         },
         "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",
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               ]
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         },
         "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",
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         },
         "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.",
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         },
         "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.",
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         },
         "title": "NumericalSolveSolverMetricMaxEstimatorVariance",
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      "NumericalSolveSolverMetricNormEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
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      "NumericalSolveSolverMetricNormEstimatorVarianceConfig": {
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         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
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               "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.",
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         },
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         "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
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         "title": "NumericalSolveSqp",
         "type": "object"
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         "additionalProperties": false,
         "description": "Specify a solution mode that excludes the pilot cost from sample allocation logic",
         "properties": {
            "offline_pilot": {
               "$ref": "#/$defs/OfflinePilotConfig",
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         },
         "title": "OfflinePilot",
         "type": "object"
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         "additionalProperties": false,
         "description": "Specify a solution mode that excludes the pilot cost from sample allocation logic",
         "properties": {
            "final_statistics": {
               "anyOf": [
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsEstimatorPerformance"
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                     "$ref": "#/$defs/OfflinePilotFinalStatisticsQoiStatistics"
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               "default": null,
               "description": "Indicate the type of final statistics to be returned by a UQ method",
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         },
         "title": "OfflinePilotConfig",
         "type": "object"
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      "OfflinePilotFinalStatisticsQoiStatistics": {
         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
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            "qoi_statistics": {
               "$ref": "#/$defs/OfflinePilotFinalStatisticsQoiStatisticsConfig",
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         "required": [
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         "title": "OfflinePilotFinalStatisticsQoiStatistics",
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      "OfflinePilotFinalStatisticsQoiStatisticsConfig": {
         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsNone"
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard"
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsCentral"
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsDistributionComplementary"
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               ],
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         },
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         "description": "Specify a solution mode that estimates performance based on projecting initial correlation/variance estimates from an offline pilot sample",
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            "offline_projection": {
               "const": true,
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               "description": "Specify a solution mode that estimates performance based on projecting initial correlation/variance estimates from an offline pilot sample",
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         },
         "title": "OfflineProjection",
         "type": "object"
      },
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         "additionalProperties": false,
         "description": "Specify a solution mode that includes the pilot cost within the sample allocation logic",
         "properties": {
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               "$ref": "#/$defs/OnlinePilotConfig",
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         },
         "title": "OnlinePilot",
         "type": "object"
      },
      "OnlinePilotConfig": {
         "additionalProperties": false,
         "description": "Specify a solution mode that includes the pilot cost within the sample allocation logic",
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               "anyOf": [
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                     "$ref": "#/$defs/OnlinePilotRelaxationFactorSequence"
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                  {
                     "$ref": "#/$defs/OnlinePilotRelaxationFixedFactor"
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                     "$ref": "#/$defs/OnlinePilotRelaxationRecursiveFactor"
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                  {
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               "description": "For an online pilot mode, apply under-relaxation to the shared sample increments",
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                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatistics"
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                  {
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               ],
               "default": null,
               "description": "Indicate the type of final statistics to be returned by a UQ method",
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         },
         "title": "OnlinePilotConfig",
         "type": "object"
      },
      "OnlinePilotFinalStatisticsEstimatorPerformance": {
         "additionalProperties": false,
         "description": "Return estimator performance as the final results of a UQ method",
         "properties": {
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               "const": true,
               "default": true,
               "description": "Return estimator performance as the final results of a UQ method",
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               "type": "boolean",
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               ]
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         },
         "title": "OnlinePilotFinalStatisticsEstimatorPerformance",
         "type": "object"
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      "OnlinePilotFinalStatisticsQoiStatistics": {
         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
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      "OnlinePilotFinalStatisticsQoiStatisticsConfig": {
         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsNone"
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsCentral"
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            },
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                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsDistributionComplementary"
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         },
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         "type": "object"
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         "description": "Placeholder for future capabilities",
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               "description": "Placeholder for future capabilities",
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         },
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      "OnlinePilotFinalStatisticsQoiStatisticsDistributionCumulative": {
         "additionalProperties": false,
         "description": "Placeholder for future capabilities",
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               "const": true,
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               "description": "Placeholder for future capabilities",
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         },
         "title": "OnlinePilotFinalStatisticsQoiStatisticsDistributionCumulative",
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      "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsCentral": {
         "additionalProperties": false,
         "description": "Output central moments and include them within the set of final statistics.",
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               "const": true,
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               "description": "Output central moments and include them within the set of final statistics.",
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         },
         "title": "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsCentral",
         "type": "object"
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         "additionalProperties": false,
         "description": "Omit moments from the set of final statistics.",
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               "const": true,
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               "description": "Omit moments from the set of final statistics.",
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               "type": "boolean",
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         },
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         "type": "object"
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      "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard": {
         "additionalProperties": false,
         "description": "Output standardized moments and include them within the set of final statistics.",
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               "description": "Output standardized moments and include them within the set of final statistics.",
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         },
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         "additionalProperties": false,
         "description": "For under-relaxation of shared sample increments, apply a sequence of factors, one per iteration",
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               "title": "Factor Sequence",
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         },
         "required": [
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         "title": "OnlinePilotRelaxationFactorSequence",
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         "additionalProperties": false,
         "description": "For under-relaxation of shared sample increments, apply a fixed factor that is invariant with iteration",
         "properties": {
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               "description": "For under-relaxation of shared sample increments, apply a fixed factor that is invariant with iteration",
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         },
         "required": [
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         "title": "OnlinePilotRelaxationFixedFactor",
         "type": "object"
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      "OnlinePilotRelaxationRecursiveFactor": {
         "additionalProperties": false,
         "description": "For under-relaxation of shared sample increments, apply a recursive factor on each iteration that advances the relaxation factor toward 1",
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               "description": "For under-relaxation of shared sample increments, apply a recursive factor on each iteration that advances the relaxation factor toward 1",
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         },
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         "title": "OnlinePilotRelaxationRecursiveFactor",
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         "additionalProperties": false,
         "description": "Specify a solution mode that estimates performance based on projecting initial correlation / covariance estimates from an online pilot sample",
         "properties": {
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               "const": true,
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               "description": "Specify a solution mode that estimates performance based on projecting initial correlation / covariance estimates from an online pilot sample",
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               ]
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         },
         "title": "OnlineProjection",
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         "additionalProperties": false,
         "description": "Replace MFMC analytic allocation with a numerical solution",
         "properties": {
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               "const": true,
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               "description": "Replace MFMC analytic allocation with a numerical solution",
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               "type": "boolean",
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               ]
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         },
         "title": "Override",
         "type": "object"
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         "properties": {
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               ],
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            "recursion_option": {
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                  {
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      "PromotedModelSelectionContext1SearchModelGraphsFullRecursion": {
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         "description": "Perform a full recursion of all admissible DAGs for a given model ensemble",
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         },
         "title": "PromotedModelSelectionContext1SearchModelGraphsFullRecursion",
         "type": "object"
      },
      "PromotedModelSelectionContext1SearchModelGraphsNoRecursion": {
         "additionalProperties": false,
         "description": "Do not recur over admissible DAGs for a given model ensemble",
         "properties": {
            "no_recursion": {
               "const": true,
               "default": true,
               "description": "Do not recur over admissible DAGs for a given model ensemble",
               "title": "No Recursion",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.recursion",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NO_GRAPH_RECURSION"
                  }
               ]
            }
         },
         "title": "PromotedModelSelectionContext1SearchModelGraphsNoRecursion",
         "type": "object"
      },
      "Quiet": {
         "additionalProperties": false,
         "description": "Level 2 of 5 - less than normal",
         "properties": {
            "quiet": {
               "const": true,
               "default": true,
               "description": "Level 2 of 5 - less than normal",
               "title": "Quiet",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "QUIET_OUTPUT"
                  }
               ]
            }
         },
         "title": "Quiet",
         "type": "object"
      },
      "RngOptionsContext2Mt19937": {
         "additionalProperties": false,
         "description": "Generates random numbers using the Mersenne twister",
         "properties": {
            "mt19937": {
               "const": true,
               "default": true,
               "description": "Generates random numbers using the Mersenne twister",
               "title": "Mt19937",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.random_number_generator",
                     "ir_value_type": "String",
                     "storage_type": "PRESENCE_LITERAL",
                     "stored_value": "mt19937"
                  }
               ]
            }
         },
         "title": "RngOptionsContext2Mt19937",
         "type": "object"
      },
      "RngOptionsContext2Rnum2": {
         "additionalProperties": false,
         "description": "Generates pseudo-random numbers using the Pecos package",
         "properties": {
            "rnum2": {
               "const": true,
               "default": true,
               "description": "Generates pseudo-random numbers using the Pecos package",
               "title": "Rnum2",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.random_number_generator",
                     "ir_value_type": "String",
                     "storage_type": "PRESENCE_LITERAL",
                     "stored_value": "rnum2"
                  }
               ]
            }
         },
         "title": "RngOptionsContext2Rnum2",
         "type": "object"
      },
      "Silent": {
         "additionalProperties": false,
         "description": "Level 1 of 5 - minimum",
         "properties": {
            "silent": {
               "const": true,
               "default": true,
               "description": "Level 1 of 5 - minimum",
               "title": "Silent",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SILENT_OUTPUT"
                  }
               ]
            }
         },
         "title": "Silent",
         "type": "object"
      },
      "Verbose": {
         "additionalProperties": false,
         "description": "Level 4 of 5 - more than normal",
         "properties": {
            "verbose": {
               "const": true,
               "default": true,
               "description": "Level 4 of 5 - more than normal",
               "title": "Verbose",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "VERBOSE_OUTPUT"
                  }
               ]
            }
         },
         "title": "Verbose",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "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:

• convergence_tolerance (dakota.spec.shared.core.MethodConvergenceTolWithTypeContext3ConvergenceTol | None)

• export_sample_sequence (dakota.spec.method.multifidelity_sampling.MultifidelitySamplingExportSampleSequence | None)

• final_solutions (int)

• fixed_seed (Literal[True] | None)

• id_method (str | None)

• max_function_evaluations (int)

• max_iterations (int)

• model_pointer (str | None)

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

• output (dakota.spec.shared.misc.Debug | dakota.spec.shared.misc.Verbose | dakota.spec.shared.misc.Normal | dakota.spec.shared.misc.Quiet | dakota.spec.shared.misc.Silent)

• pilot_samples (list[int] | None)

• rng (dakota.spec.shared.misc.RngOptionsContext2Mt19937 | dakota.spec.shared.misc.RngOptionsContext2Rnum2)

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

• search_model_graphs (dakota.spec.shared.misc.PromotedModelSelectionContext1SearchModelGraphs | None)

• seed_sequence (list[int] | None)

• solution_mode (dakota.spec.shared.misc.OnlinePilot | dakota.spec.shared.misc.OfflinePilot | dakota.spec.shared.misc.OnlineProjection | dakota.spec.shared.misc.OfflineProjection)

field convergence_tolerance: MethodConvergenceTolWithTypeContext3ConvergenceTol | None = None

Stopping criterion based on relative error reduction

field export_sample_sequence: MultifidelitySamplingExportSampleSequence | None = None

Enable export of multilevel/multifidelity sample sequences to individual files

field final_solutions: int = 0

Number of designs returned as the best solutions

Constraints:

• ge = 0

field fixed_seed: Literal[True] | None = None

Reuses the same seed value for multiple random sampling sets

field id_method: str | None = None

Name the method block; helpful when there are multiple

field max_function_evaluations: int = 9223372036854775807

Stopping criterion based on maximum function evaluations

Constraints:

• ge = 0

field max_iterations: int = 9223372036854775807

Number of iterations allowed for optimizers and adaptive UQ methods

Constraints:

• ge = 0

field model_pointer: str | None = None

Identifier for model block to be used by a method

field numerical_solve: NumericalSolve | None = None

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

field output: Debug | Verbose | Normal | Quiet | Silent [Optional]

Control how much method information is written to the screen and output file

field pilot_samples: list[int] | None = None

Initial set of samples for multilevel/multifidelity sampling methods.

field rng: RngOptionsContext2Mt19937 | RngOptionsContext2Rnum2 [Optional]

Selection of a random number generator

field sample_type: MethodSampleTypeLhsMcLhs | MethodSampleTypeLhsMcRandom | None = None

Selection of sampling strategy

field search_model_graphs: PromotedModelSelectionContext1SearchModelGraphs | None = None

Perform a search over admissible model relationships for a given model ensemble

field seed_sequence: list[int] | None = None

Sequence of seed values for multi-stage random sampling

field solution_mode: OnlinePilot | OfflinePilot | OnlineProjection | OfflineProjection [Optional]

Solution mode for multilevel/multifidelity methods

Generated Pydantic models for method.multifidelity_sampling

pydantic model dakota.spec.method.multifidelity_sampling.MultifidelitySamplingExportSampleSequence

Enable export of multilevel/multifidelity sample sequences to individual files

Show JSON schema

{
   "title": "MultifidelitySamplingExportSampleSequence",
   "description": "Enable export of multilevel/multifidelity sample sequences to individual files",
   "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