multilevel_sampling

pydantic model dakota.spec.method.multilevel_sampling.MultilevelSamplingSelection

Generated model for MultilevelSamplingSelection

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{
   "title": "MultilevelSamplingSelection",
   "description": "Generated model for MultilevelSamplingSelection",
   "type": "object",
   "properties": {
      "multilevel_sampling": {
         "$ref": "#/$defs/MultilevelSamplingConfig",
         "x-aliases": [
            "multilevel_mc",
            "mlmc"
         ],
         "x-materialization": [
            {
               "ir_key": "method.algorithm",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "MULTILEVEL_SAMPLING"
            }
         ]
      }
   },
   "$defs": {
      "CostConstraint": {
         "additionalProperties": false,
         "description": "Allocate samples to target specified cost",
         "properties": {
            "cost_constraint": {
               "const": true,
               "default": true,
               "description": "Allocate samples to target specified cost",
               "title": "Cost Constraint",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.convergence_tolerance_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "COST_CONSTRAINT_TARGET"
                  }
               ]
            }
         },
         "title": "CostConstraint",
         "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",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "DEBUG_OUTPUT"
                  }
               ]
            }
         },
         "title": "Debug",
         "type": "object"
      },
      "Max": {
         "additionalProperties": false,
         "description": "Compute sample allocation for each response and use maximum over responses for each level in a MLMC algorithm",
         "properties": {
            "max": {
               "const": true,
               "default": true,
               "description": "Compute sample allocation for each response and use maximum over responses for each level in a MLMC algorithm",
               "title": "Max",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.qoi_aggregation",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "QOI_AGGREGATION_MAX"
                  }
               ]
            }
         },
         "title": "Max",
         "type": "object"
      },
      "Mean": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the mean.",
         "properties": {
            "mean": {
               "const": true,
               "default": true,
               "description": "Fit MLMC sample allocation to control the variance of the estimator for the mean.",
               "title": "Mean",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TARGET_MEAN"
                  }
               ]
            }
         },
         "title": "Mean",
         "type": "object"
      },
      "MethodConvergenceTolWithTypeContext1Absolute": {
         "additionalProperties": false,
         "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods",
         "properties": {
            "absolute": {
               "const": true,
               "default": true,
               "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods",
               "title": "Absolute",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.convergence_tolerance_type",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "ABSOLUTE_CONVERGENCE_TOLERANCE"
                  }
               ]
            }
         },
         "title": "MethodConvergenceTolWithTypeContext1Absolute",
         "type": "object"
      },
      "MethodConvergenceTolWithTypeContext1ConvergenceTol": {
         "additionalProperties": false,
         "description": "Stopping criterion based on relative error",
         "properties": {
            "value": {
               "default": -1.7976931348623157e+308,
               "description": "Stopping criterion based on relative error",
               "title": "Value",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.convergence_tolerance",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  },
                  {
                     "ir_key": "method.jega.percent_change",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "convergence_tolerance_type": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodConvergenceTolWithTypeContext1Relative"
                  },
                  {
                     "$ref": "#/$defs/MethodConvergenceTolWithTypeContext1Absolute"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Convergence tolerance type",
               "title": "Convergence Tolerance Type",
               "x-union-pattern": 2
            }
         },
         "title": "MethodConvergenceTolWithTypeContext1ConvergenceTol",
         "type": "object"
      },
      "MethodConvergenceTolWithTypeContext1Relative": {
         "additionalProperties": false,
         "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark",
         "properties": {
            "relative": {
               "const": true,
               "default": true,
               "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark",
               "title": "Relative",
               "type": "boolean",
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                  {
                     "ir_key": "method.nond.convergence_tolerance_type",
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                     "storage_type": "PRESENCE_ENUM",
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                  }
               ]
            }
         },
         "title": "MethodConvergenceTolWithTypeContext1Relative",
         "type": "object"
      },
      "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",
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               ]
            }
         },
         "title": "MethodExportSamplesFormatAnnotated",
         "type": "object"
      },
      "MethodExportSamplesFormatCustomAnnotated": {
         "additionalProperties": false,
         "description": "Selects custom-annotated tabular file format",
         "properties": {
            "custom_annotated": {
               "$ref": "#/$defs/MethodExportSamplesFormatCustomAnnotatedConfig",
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                  {
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                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
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                  }
               ],
               "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,
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               "title": "Header",
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                     "storage_type": "AUGMENT_ENUM",
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                  }
               ]
            },
            "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": [
                  {
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                     "ir_value_type": "unsigned short",
                     "storage_type": "AUGMENT_ENUM",
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               ]
            },
            "interface_id": {
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                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
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               "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",
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                  }
               ]
            }
         },
         "title": "MethodExportSamplesFormatFreeform",
         "type": "object"
      },
      "MethodMlmfSolverMetricAverageEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "average_estimator_variance": {
               "const": true,
               "default": true,
               "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
               "title": "Average Estimator Variance",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "AVG_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "title": "MethodMlmfSolverMetricAverageEstimatorVariance",
         "type": "object"
      },
      "MethodMlmfSolverMetricMaxEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "max_estimator_variance": {
               "const": true,
               "default": true,
               "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
               "title": "Max Estimator Variance",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "MAX_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "title": "MethodMlmfSolverMetricMaxEstimatorVariance",
         "type": "object"
      },
      "MethodMlmfSolverMetricNormEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "norm_estimator_variance": {
               "$ref": "#/$defs/MethodMlmfSolverMetricNormEstimatorVarianceConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NORM_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "required": [
            "norm_estimator_variance"
         ],
         "title": "MethodMlmfSolverMetricNormEstimatorVariance",
         "type": "object"
      },
      "MethodMlmfSolverMetricNormEstimatorVarianceConfig": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "norm_order": {
               "default": 2.0,
               "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.",
               "minimum": 1.0,
               "title": "Norm Order",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric_norm_order",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "title": "MethodMlmfSolverMetricNormEstimatorVarianceConfig",
         "type": "object"
      },
      "MethodMlmfSubProblemSolverCompetedLocal": {
         "additionalProperties": false,
         "description": "Use a competed local solver scheme for solving an optimization sub-problem",
         "properties": {
            "competed_local": {
               "const": true,
               "default": true,
               "description": "Use a competed local solver scheme for solving an optimization sub-problem",
               "title": "Competed Local",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_NPSOL_OPTPP"
                  }
               ]
            }
         },
         "title": "MethodMlmfSubProblemSolverCompetedLocal",
         "type": "object"
      },
      "MethodMlmfSubProblemSolverGlobalLocal": {
         "additionalProperties": false,
         "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
         "properties": {
            "global_local": {
               "const": true,
               "default": true,
               "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
               "title": "Global Local",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_DIRECT_NPSOL_OPTPP"
                  }
               ]
            }
         },
         "title": "MethodMlmfSubProblemSolverGlobalLocal",
         "type": "object"
      },
      "MethodMlmfSubProblemSolverNip": {
         "additionalProperties": false,
         "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
         "properties": {
            "nip": {
               "const": true,
               "default": true,
               "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
               "title": "Nip",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_OPTPP"
                  }
               ]
            }
         },
         "title": "MethodMlmfSubProblemSolverNip",
         "type": "object"
      },
      "MethodMlmfSubProblemSolverSqp": {
         "additionalProperties": false,
         "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
         "properties": {
            "sqp": {
               "const": true,
               "default": true,
               "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
               "title": "Sqp",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_NPSOL"
                  }
               ]
            }
         },
         "title": "MethodMlmfSubProblemSolverSqp",
         "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",
                     "stored_value": "SUBMETHOD_LHS"
                  }
               ]
            }
         },
         "title": "MethodSampleTypeLhsMcLhs",
         "type": "object"
      },
      "MethodSampleTypeLhsMcRandom": {
         "additionalProperties": false,
         "description": "Uses purely random Monte Carlo sampling to sample variables",
         "properties": {
            "random": {
               "const": true,
               "default": true,
               "description": "Uses purely random Monte Carlo sampling to sample variables",
               "title": "Random",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.sample_type",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
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                  }
               ]
            }
         },
         "title": "MethodSampleTypeLhsMcRandom",
         "type": "object"
      },
      "MultilevelSamplingConfig": {
         "additionalProperties": false,
         "description": "Multilevel Monte Carlo (MLMC) 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": "#/$defs/RngOptionsContext2Rnum2"
                  }
               ],
               "description": "Selection of a random number generator",
               "title": "Rng",
               "x-model-default": "RngOptionsContext2Mt19937",
               "x-union-pattern": 1
            },
            "max_function_evaluations": {
               "default": 9223372036854775807,
               "description": "Stopping criterion based on maximum function evaluations",
               "minimum": 0,
               "title": "Max Function Evaluations",
               "type": "integer",
               "x-materialization": [
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                     "storage_type": "DIRECT_VALUE"
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               ]
            },
            "max_iterations": {
               "default": 9223372036854775807,
               "description": "Stopping criterion based on number of refinement iterations within the multilevel sample allocation",
               "minimum": 0,
               "title": "Max Iterations",
               "type": "integer",
               "x-materialization": [
                  {
                     "ir_key": "method.max_iterations",
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               ]
            },
            "convergence_tolerance": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodConvergenceTolWithTypeContext1ConvergenceTol"
                  },
                  {
                     "type": "null"
                  }
               ],
               "argument": "value",
               "default": null,
               "description": "Stopping criterion based on relative error"
            },
            "sample_type": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodSampleTypeLhsMcLhs"
                  },
                  {
                     "$ref": "#/$defs/MethodSampleTypeLhsMcRandom"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Selection of sampling strategy",
               "title": "Sample Type",
               "x-union-pattern": 2
            },
            "solution_mode": {
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                  {
                     "$ref": "#/$defs/OnlinePilot"
                  },
                  {
                     "$ref": "#/$defs/OfflinePilot"
                  },
                  {
                     "$ref": "#/$defs/OnlineProjection"
                  },
                  {
                     "$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": {
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                     "items": {
                        "type": "integer"
                     },
                     "type": "array"
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                  {
                     "type": "null"
                  }
               ],
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               "description": "Initial set of samples for multilevel sampling methods.",
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               "x-aliases": [
                  "initial_samples"
               ],
               "x-materialization": [
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                     "ir_key": "method.nond.pilot_samples",
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               ]
            },
            "seed_sequence": {
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                     "items": {
                        "type": "integer"
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                     "type": "array"
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               ],
               "default": null,
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               "title": "Seed Sequence",
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                     "storage_type": "DIRECT_VALUE"
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            },
            "fixed_seed": {
               "anyOf": [
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                     "const": true,
                     "type": "boolean"
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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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               ]
            },
            "id_method": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Name the method block; helpful when there are multiple",
               "title": "Id Method",
               "x-materialization": [
                  {
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                     "ir_value_type": "String",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "output": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/Debug"
                  },
                  {
                     "$ref": "#/$defs/Verbose"
                  },
                  {
                     "$ref": "#/$defs/Normal"
                  },
                  {
                     "$ref": "#/$defs/Quiet"
                  },
                  {
                     "$ref": "#/$defs/Silent"
                  }
               ],
               "description": "Control how much method information is written to the screen and output file",
               "title": "Output",
               "x-model-default": "Normal",
               "x-union-pattern": 1
            },
            "final_solutions": {
               "default": 0,
               "description": "Number of designs returned as the best solutions",
               "minimum": 0,
               "title": "Final Solutions",
               "type": "integer",
               "x-materialization": [
                  {
                     "ir_key": "method.final_solutions",
                     "ir_value_type": "size_t",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "weighted": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/Weighted"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Include control variate weights for each of the recursive differences using in multilevel Monte Carlo (MLMC)",
               "x-materialization": [
                  {
                     "ir_key": "method.sub_method",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_WEIGHTED_MLMC"
                  }
               ]
            },
            "export_sample_sequence": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/MultilevelSamplingExportSampleSequence"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Enable export of multilevel/multifidelity sample sequences to individual files",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.export_sample_sequence",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            },
            "allocation_target": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/Mean"
                  },
                  {
                     "$ref": "#/$defs/Variance"
                  },
                  {
                     "$ref": "#/$defs/StandardDeviation"
                  },
                  {
                     "$ref": "#/$defs/Scalarization"
                  }
               ],
               "description": "Allocation statistics/target for the MLMC sample allocation.",
               "title": "Allocation Target",
               "x-model-default": "Mean",
               "x-union-pattern": 1
            },
            "qoi_aggregation": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/Sum"
                  },
                  {
                     "$ref": "#/$defs/Max"
                  }
               ],
               "description": "Aggregation strategy for the QoIs statistics for problems with multiple responses in the MLMC algorithm",
               "title": "Qoi Aggregation",
               "x-model-default": "Sum",
               "x-union-pattern": 1
            },
            "convergence_tolerance_target": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/VarianceConstraint"
                  },
                  {
                     "$ref": "#/$defs/CostConstraint"
                  }
               ],
               "description": "Select target for MLMC sample allocation",
               "title": "Convergence Tolerance Target",
               "x-model-default": "VarianceConstraint",
               "x-union-pattern": 1
            }
         },
         "title": "MultilevelSamplingConfig",
         "type": "object",
         "x-model-validations": [
            {
               "validationContext": "mlmfpilotsamplescontext1mixin",
               "validationErrorMessage": "For mlmfpilotsamplescontext1mixin, all elements of pilot_samples must be >= 0.",
               "validationFields": [
                  "pilot_samples"
               ],
               "validationLiterals": [],
               "validationRuleName": "check_nonnegative_list"
            }
         ]
      },
      "MultilevelSamplingExportSampleSequence": {
         "additionalProperties": false,
         "description": "Enable export of multilevel/multifidelity sample sequences to individual files",
         "properties": {
            "format": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodExportSamplesFormatCustomAnnotated"
                  },
                  {
                     "$ref": "#/$defs/MethodExportSamplesFormatAnnotated"
                  },
                  {
                     "$ref": "#/$defs/MethodExportSamplesFormatFreeform"
                  }
               ],
               "description": "Tabular Format",
               "title": "Format",
               "x-model-default": "MethodExportSamplesFormatAnnotated",
               "x-union-pattern": 1
            }
         },
         "title": "MultilevelSamplingExportSampleSequence",
         "type": "object"
      },
      "Normal": {
         "additionalProperties": false,
         "description": "Level 3 of 5 - default",
         "properties": {
            "normal": {
               "const": true,
               "default": true,
               "description": "Level 3 of 5 - default",
               "title": "Normal",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NORMAL_OUTPUT"
                  }
               ]
            }
         },
         "title": "Normal",
         "type": "object"
      },
      "OfflinePilot": {
         "additionalProperties": false,
         "description": "Specify a solution mode that excludes the pilot cost from sample allocation logic",
         "properties": {
            "offline_pilot": {
               "$ref": "#/$defs/OfflinePilotConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.ensemble_pilot_solution_mode",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "OFFLINE_PILOT"
                  }
               ],
               "x-model-default": "OfflinePilotConfig"
            }
         },
         "title": "OfflinePilot",
         "type": "object"
      },
      "OfflinePilotConfig": {
         "additionalProperties": false,
         "description": "Specify a solution mode that excludes the pilot cost from sample allocation logic",
         "properties": {
            "final_statistics": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsEstimatorPerformance"
                  },
                  {
                     "$ref": "#/$defs/OfflinePilotFinalStatisticsQoiStatistics"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Indicate the type of final statistics to be returned by a UQ method",
               "title": "Final Statistics",
               "x-union-pattern": 2
            }
         },
         "title": "OfflinePilotConfig",
         "type": "object"
      },
      "OfflinePilotFinalStatisticsQoiStatistics": {
         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
         "properties": {
            "qoi_statistics": {
               "$ref": "#/$defs/OfflinePilotFinalStatisticsQoiStatisticsConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.final_statistics",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "QOI_STATISTICS"
                  }
               ]
            }
         },
         "required": [
            "qoi_statistics"
         ],
         "title": "OfflinePilotFinalStatisticsQoiStatistics",
         "type": "object"
      },
      "OfflinePilotFinalStatisticsQoiStatisticsConfig": {
         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
         "properties": {
            "final_moments": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsNone"
                  },
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard"
                  },
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsCentral"
                  }
               ],
               "description": "Output moments of the specified type and include them within the set of final statistics.",
               "title": "Final Moments",
               "x-model-default": "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard",
               "x-union-pattern": 1
            },
            "distribution": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsDistributionCumulative"
                  },
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsDistributionComplementary"
                  }
               ],
               "description": "Placeholder for future capabilities",
               "title": "Distribution",
               "x-model-default": "OnlinePilotFinalStatisticsQoiStatisticsDistributionCumulative",
               "x-union-pattern": 1
            }
         },
         "title": "OfflinePilotFinalStatisticsQoiStatisticsConfig",
         "type": "object"
      },
      "OfflineProjection": {
         "additionalProperties": false,
         "description": "Specify a solution mode that estimates performance based on projecting initial correlation/variance estimates from an offline pilot sample",
         "properties": {
            "offline_projection": {
               "const": true,
               "default": true,
               "description": "Specify a solution mode that estimates performance based on projecting initial correlation/variance estimates from an offline pilot sample",
               "title": "Offline Projection",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.ensemble_pilot_solution_mode",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "OFFLINE_PILOT_PROJECTION"
                  }
               ]
            }
         },
         "title": "OfflineProjection",
         "type": "object"
      },
      "OnlinePilot": {
         "additionalProperties": false,
         "description": "Specify a solution mode that includes the pilot cost within the sample allocation logic",
         "properties": {
            "online_pilot": {
               "$ref": "#/$defs/OnlinePilotConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.ensemble_pilot_solution_mode",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "ONLINE_PILOT"
                  }
               ],
               "x-model-default": "OnlinePilotConfig"
            }
         },
         "title": "OnlinePilot",
         "type": "object"
      },
      "OnlinePilotConfig": {
         "additionalProperties": false,
         "description": "Specify a solution mode that includes the pilot cost within the sample allocation logic",
         "properties": {
            "relaxation": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/OnlinePilotRelaxationFactorSequence"
                  },
                  {
                     "$ref": "#/$defs/OnlinePilotRelaxationFixedFactor"
                  },
                  {
                     "$ref": "#/$defs/OnlinePilotRelaxationRecursiveFactor"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "For an online pilot mode, apply under-relaxation to the shared sample increments",
               "title": "Relaxation",
               "x-union-pattern": 2
            },
            "final_statistics": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsEstimatorPerformance"
                  },
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatistics"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Indicate the type of final statistics to be returned by a UQ method",
               "title": "Final Statistics",
               "x-union-pattern": 2
            }
         },
         "title": "OnlinePilotConfig",
         "type": "object"
      },
      "OnlinePilotFinalStatisticsEstimatorPerformance": {
         "additionalProperties": false,
         "description": "Return estimator performance as the final results of a UQ method",
         "properties": {
            "estimator_performance": {
               "const": true,
               "default": true,
               "description": "Return estimator performance as the final results of a UQ method",
               "title": "Estimator Performance",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.final_statistics",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "ESTIMATOR_PERFORMANCE"
                  }
               ]
            }
         },
         "title": "OnlinePilotFinalStatisticsEstimatorPerformance",
         "type": "object"
      },
      "OnlinePilotFinalStatisticsQoiStatistics": {
         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
         "properties": {
            "qoi_statistics": {
               "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.final_statistics",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "QOI_STATISTICS"
                  }
               ]
            }
         },
         "required": [
            "qoi_statistics"
         ],
         "title": "OnlinePilotFinalStatisticsQoiStatistics",
         "type": "object"
      },
      "OnlinePilotFinalStatisticsQoiStatisticsConfig": {
         "additionalProperties": false,
         "description": "Return the quantity of interest (QoI) statistics as the final results of a UQ method",
         "properties": {
            "final_moments": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsNone"
                  },
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard"
                  },
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsCentral"
                  }
               ],
               "description": "Output moments of the specified type and include them within the set of final statistics.",
               "title": "Final Moments",
               "x-model-default": "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard",
               "x-union-pattern": 1
            },
            "distribution": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsDistributionCumulative"
                  },
                  {
                     "$ref": "#/$defs/OnlinePilotFinalStatisticsQoiStatisticsDistributionComplementary"
                  }
               ],
               "description": "Placeholder for future capabilities",
               "title": "Distribution",
               "x-model-default": "OnlinePilotFinalStatisticsQoiStatisticsDistributionCumulative",
               "x-union-pattern": 1
            }
         },
         "title": "OnlinePilotFinalStatisticsQoiStatisticsConfig",
         "type": "object"
      },
      "OnlinePilotFinalStatisticsQoiStatisticsDistributionComplementary": {
         "additionalProperties": false,
         "description": "Placeholder for future capabilities",
         "properties": {
            "complementary": {
               "const": true,
               "default": true,
               "description": "Placeholder for future capabilities",
               "title": "Complementary",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.distribution",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "COMPLEMENTARY"
                  }
               ]
            }
         },
         "title": "OnlinePilotFinalStatisticsQoiStatisticsDistributionComplementary",
         "type": "object"
      },
      "OnlinePilotFinalStatisticsQoiStatisticsDistributionCumulative": {
         "additionalProperties": false,
         "description": "Placeholder for future capabilities",
         "properties": {
            "cumulative": {
               "const": true,
               "default": true,
               "description": "Placeholder for future capabilities",
               "title": "Cumulative",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.distribution",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "CUMULATIVE"
                  }
               ]
            }
         },
         "title": "OnlinePilotFinalStatisticsQoiStatisticsDistributionCumulative",
         "type": "object"
      },
      "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsCentral": {
         "additionalProperties": false,
         "description": "Output central moments and include them within the set of final statistics.",
         "properties": {
            "central": {
               "const": true,
               "default": true,
               "description": "Output central moments and include them within the set of final statistics.",
               "title": "Central",
               "type": "boolean",
               "x-materialization": [
                  {
                     "enum_scope": "Pecos",
                     "ir_key": "method.nond.final_moments",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "CENTRAL_MOMENTS"
                  }
               ]
            }
         },
         "title": "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsCentral",
         "type": "object"
      },
      "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsNone": {
         "additionalProperties": false,
         "description": "Omit moments from the set of final statistics.",
         "properties": {
            "none": {
               "const": true,
               "default": true,
               "description": "Omit moments from the set of final statistics.",
               "title": "None",
               "type": "boolean",
               "x-materialization": [
                  {
                     "enum_scope": "Pecos",
                     "ir_key": "method.nond.final_moments",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NO_MOMENTS"
                  }
               ]
            }
         },
         "title": "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsNone",
         "type": "object"
      },
      "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard": {
         "additionalProperties": false,
         "description": "Output standardized moments and include them within the set of final statistics.",
         "properties": {
            "standard": {
               "const": true,
               "default": true,
               "description": "Output standardized moments and include them within the set of final statistics.",
               "title": "Standard",
               "type": "boolean",
               "x-materialization": [
                  {
                     "enum_scope": "Pecos",
                     "ir_key": "method.nond.final_moments",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "STANDARD_MOMENTS"
                  }
               ]
            }
         },
         "title": "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard",
         "type": "object"
      },
      "OnlinePilotRelaxationFactorSequence": {
         "additionalProperties": false,
         "description": "For under-relaxation of shared sample increments, apply a sequence of factors, one per iteration",
         "properties": {
            "factor_sequence": {
               "description": "For under-relaxation of shared sample increments, apply a sequence of factors, one per iteration",
               "items": {
                  "type": "number"
               },
               "title": "Factor Sequence",
               "type": "array",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.relaxation.factor_sequence",
                     "ir_value_type": "RealVector",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "required": [
            "factor_sequence"
         ],
         "title": "OnlinePilotRelaxationFactorSequence",
         "type": "object"
      },
      "OnlinePilotRelaxationFixedFactor": {
         "additionalProperties": false,
         "description": "For under-relaxation of shared sample increments, apply a fixed factor that is invariant with iteration",
         "properties": {
            "fixed_factor": {
               "description": "For under-relaxation of shared sample increments, apply a fixed factor that is invariant with iteration",
               "title": "Fixed Factor",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.relaxation.fixed_factor",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "required": [
            "fixed_factor"
         ],
         "title": "OnlinePilotRelaxationFixedFactor",
         "type": "object"
      },
      "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",
         "properties": {
            "recursive_factor": {
               "description": "For under-relaxation of shared sample increments, apply a recursive factor on each iteration that advances the relaxation factor toward 1",
               "title": "Recursive Factor",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.relaxation.recursive_factor",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "required": [
            "recursive_factor"
         ],
         "title": "OnlinePilotRelaxationRecursiveFactor",
         "type": "object"
      },
      "OnlineProjection": {
         "additionalProperties": false,
         "description": "Specify a solution mode that estimates performance based on projecting initial correlation / covariance estimates from an online pilot sample",
         "properties": {
            "online_projection": {
               "const": true,
               "default": true,
               "description": "Specify a solution mode that estimates performance based on projecting initial correlation / covariance estimates from an online pilot sample",
               "title": "Online Projection",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.ensemble_pilot_solution_mode",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "ONLINE_PILOT_PROJECTION"
                  }
               ]
            }
         },
         "title": "OnlineProjection",
         "type": "object"
      },
      "PromotedModelSelectionContext2SearchModelGraphs": {
         "additionalProperties": false,
         "description": "For weighted multilevel Monte Carlo, this option activates a search over possible hierarchical model graphs",
         "properties": {
            "model_selection": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Perform a recursion of admissible model subsets for a given model ensemble",
               "title": "Model Selection",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.selection",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "ALL_MODEL_COMBINATIONS"
                  }
               ]
            },
            "recursion_option": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/PromotedModelSelectionContext2SearchModelGraphsNoRecursion"
                  },
                  {
                     "$ref": "#/$defs/PromotedModelSelectionContext2SearchModelGraphsFullRecursion"
                  }
               ],
               "description": "DAG Ensemble Generation Option",
               "title": "Recursion Option",
               "x-union-pattern": 4
            }
         },
         "required": [
            "recursion_option"
         ],
         "title": "PromotedModelSelectionContext2SearchModelGraphs",
         "type": "object"
      },
      "PromotedModelSelectionContext2SearchModelGraphsFullRecursion": {
         "additionalProperties": false,
         "description": "Perform a full recursion of all admissible DAGs for a given model ensemble",
         "properties": {
            "full_recursion": {
               "const": true,
               "default": true,
               "description": "Perform a full recursion of all admissible DAGs for a given model ensemble",
               "title": "Full Recursion",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.recursion",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "FULL_GRAPH_RECURSION"
                  }
               ]
            }
         },
         "title": "PromotedModelSelectionContext2SearchModelGraphsFullRecursion",
         "type": "object"
      },
      "PromotedModelSelectionContext2SearchModelGraphsNoRecursion": {
         "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": "PromotedModelSelectionContext2SearchModelGraphsNoRecursion",
         "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"
      },
      "Scalarization": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to a mixture of terms of means and standard deviations.",
         "properties": {
            "scalarization": {
               "$ref": "#/$defs/ScalarizationConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TARGET_SCALARIZATION"
                  }
               ],
               "x-model-default": "ScalarizationConfig"
            }
         },
         "title": "Scalarization",
         "type": "object"
      },
      "ScalarizationConfig": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to a mixture of terms of means and standard deviations.",
         "properties": {
            "scalarization_response_mapping": {
               "anyOf": [
                  {
                     "items": {
                        "type": "number"
                     },
                     "type": "array"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Coefficients for linear scalarization (combination) of responses",
               "title": "Scalarization Response Mapping",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.scalarization_response_mapping",
                     "ir_value_type": "RealVector",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "optimization": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the scalarization.",
               "title": "Optimization",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target.optimization",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            }
         },
         "title": "ScalarizationConfig",
         "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"
      },
      "StandardDeviation": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.",
         "properties": {
            "standard_deviation": {
               "$ref": "#/$defs/StandardDeviationConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TARGET_SIGMA"
                  }
               ],
               "x-model-default": "StandardDeviationConfig"
            }
         },
         "title": "StandardDeviation",
         "type": "object"
      },
      "StandardDeviationConfig": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.",
         "properties": {
            "optimization": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the standard_deviation.",
               "title": "Optimization",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target.optimization",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            }
         },
         "title": "StandardDeviationConfig",
         "type": "object"
      },
      "Sum": {
         "additionalProperties": false,
         "description": "Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm.",
         "properties": {
            "sum": {
               "const": true,
               "default": true,
               "description": "Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm.",
               "title": "Sum",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.qoi_aggregation",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "QOI_AGGREGATION_SUM"
                  }
               ]
            }
         },
         "title": "Sum",
         "type": "object"
      },
      "Variance": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the variance.",
         "properties": {
            "variance": {
               "$ref": "#/$defs/VarianceConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TARGET_VARIANCE"
                  }
               ],
               "x-model-default": "VarianceConfig"
            }
         },
         "title": "Variance",
         "type": "object"
      },
      "VarianceConfig": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the variance.",
         "properties": {
            "optimization": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the variance.",
               "title": "Optimization",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target.optimization",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            }
         },
         "title": "VarianceConfig",
         "type": "object"
      },
      "VarianceConstraint": {
         "additionalProperties": false,
         "description": "Allocate samples to target specified variance",
         "properties": {
            "variance_constraint": {
               "const": true,
               "default": true,
               "description": "Allocate samples to target specified variance",
               "title": "Variance Constraint",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.convergence_tolerance_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "VARIANCE_CONSTRAINT_TARGET"
                  }
               ]
            }
         },
         "title": "VarianceConstraint",
         "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"
      },
      "Weighted": {
         "additionalProperties": false,
         "description": "Include control variate weights for each of the recursive differences using in multilevel Monte Carlo (MLMC)",
         "properties": {
            "solver_metric": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodMlmfSolverMetricAverageEstimatorVariance"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSolverMetricNormEstimatorVariance"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSolverMetricMaxEstimatorVariance"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Metric employed during numerical solutions in sampling-based multifidelity UQ methods.",
               "title": "Solver Metric",
               "x-union-pattern": 2
            },
            "optimization_solver": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodMlmfSubProblemSolverSqp"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSubProblemSolverNip"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSubProblemSolverGlobalLocal"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSubProblemSolverCompetedLocal"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Optimization Solver",
               "title": "Optimization Solver",
               "x-union-pattern": 2
            },
            "search_model_graphs": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/PromotedModelSelectionContext2SearchModelGraphs"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "For weighted multilevel Monte Carlo, this option activates a search over possible hierarchical model graphs"
            }
         },
         "title": "Weighted",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "required": [
      "multilevel_sampling"
   ]
}

Fields:

• multilevel_sampling (dakota.spec.method.multilevel_sampling.MultilevelSamplingConfig)

field multilevel_sampling: MultilevelSamplingConfig [Required]

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

Get registry, performing deferred registration on first call

classmethod get_union()

Generate Union from all registered selections

pydantic model dakota.spec.method.multilevel_sampling.MultilevelSamplingConfig

Multilevel Monte Carlo (MLMC) sampling method for UQ

Show JSON schema

{
   "title": "MultilevelSamplingConfig",
   "description": "Multilevel Monte Carlo (MLMC) sampling method for UQ",
   "type": "object",
   "properties": {
      "model_pointer": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Identifier for model block to be used by a method",
         "title": "Model Pointer",
         "x-block-pointer": "model",
         "x-materialization": [
            {
               "ir_key": "method.model_pointer",
               "ir_value_type": "String",
               "storage_type": "DIRECT_VALUE"
            }
         ]
      },
      "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": "Stopping criterion based on number of refinement iterations within the multilevel sample allocation",
         "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/MethodConvergenceTolWithTypeContext1ConvergenceTol"
            },
            {
               "type": "null"
            }
         ],
         "argument": "value",
         "default": null,
         "description": "Stopping criterion based on relative error"
      },
      "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
      },
      "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 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"
            }
         ]
      },
      "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",
               "ir_value_type": "size_t",
               "storage_type": "DIRECT_VALUE"
            }
         ]
      },
      "weighted": {
         "anyOf": [
            {
               "$ref": "#/$defs/Weighted"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Include control variate weights for each of the recursive differences using in multilevel Monte Carlo (MLMC)",
         "x-materialization": [
            {
               "ir_key": "method.sub_method",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "SUBMETHOD_WEIGHTED_MLMC"
            }
         ]
      },
      "export_sample_sequence": {
         "anyOf": [
            {
               "$ref": "#/$defs/MultilevelSamplingExportSampleSequence"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Enable export of multilevel/multifidelity sample sequences to individual files",
         "x-materialization": [
            {
               "ir_key": "method.nond.export_sample_sequence",
               "ir_value_type": "bool",
               "storage_type": "PRESENCE_TRUE"
            }
         ]
      },
      "allocation_target": {
         "anyOf": [
            {
               "$ref": "#/$defs/Mean"
            },
            {
               "$ref": "#/$defs/Variance"
            },
            {
               "$ref": "#/$defs/StandardDeviation"
            },
            {
               "$ref": "#/$defs/Scalarization"
            }
         ],
         "description": "Allocation statistics/target for the MLMC sample allocation.",
         "title": "Allocation Target",
         "x-model-default": "Mean",
         "x-union-pattern": 1
      },
      "qoi_aggregation": {
         "anyOf": [
            {
               "$ref": "#/$defs/Sum"
            },
            {
               "$ref": "#/$defs/Max"
            }
         ],
         "description": "Aggregation strategy for the QoIs statistics for problems with multiple responses in the MLMC algorithm",
         "title": "Qoi Aggregation",
         "x-model-default": "Sum",
         "x-union-pattern": 1
      },
      "convergence_tolerance_target": {
         "anyOf": [
            {
               "$ref": "#/$defs/VarianceConstraint"
            },
            {
               "$ref": "#/$defs/CostConstraint"
            }
         ],
         "description": "Select target for MLMC sample allocation",
         "title": "Convergence Tolerance Target",
         "x-model-default": "VarianceConstraint",
         "x-union-pattern": 1
      }
   },
   "$defs": {
      "CostConstraint": {
         "additionalProperties": false,
         "description": "Allocate samples to target specified cost",
         "properties": {
            "cost_constraint": {
               "const": true,
               "default": true,
               "description": "Allocate samples to target specified cost",
               "title": "Cost Constraint",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.convergence_tolerance_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "COST_CONSTRAINT_TARGET"
                  }
               ]
            }
         },
         "title": "CostConstraint",
         "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",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "DEBUG_OUTPUT"
                  }
               ]
            }
         },
         "title": "Debug",
         "type": "object"
      },
      "Max": {
         "additionalProperties": false,
         "description": "Compute sample allocation for each response and use maximum over responses for each level in a MLMC algorithm",
         "properties": {
            "max": {
               "const": true,
               "default": true,
               "description": "Compute sample allocation for each response and use maximum over responses for each level in a MLMC algorithm",
               "title": "Max",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.qoi_aggregation",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "QOI_AGGREGATION_MAX"
                  }
               ]
            }
         },
         "title": "Max",
         "type": "object"
      },
      "Mean": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the mean.",
         "properties": {
            "mean": {
               "const": true,
               "default": true,
               "description": "Fit MLMC sample allocation to control the variance of the estimator for the mean.",
               "title": "Mean",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TARGET_MEAN"
                  }
               ]
            }
         },
         "title": "Mean",
         "type": "object"
      },
      "MethodConvergenceTolWithTypeContext1Absolute": {
         "additionalProperties": false,
         "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods",
         "properties": {
            "absolute": {
               "const": true,
               "default": true,
               "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods",
               "title": "Absolute",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.convergence_tolerance_type",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "ABSOLUTE_CONVERGENCE_TOLERANCE"
                  }
               ]
            }
         },
         "title": "MethodConvergenceTolWithTypeContext1Absolute",
         "type": "object"
      },
      "MethodConvergenceTolWithTypeContext1ConvergenceTol": {
         "additionalProperties": false,
         "description": "Stopping criterion based on relative error",
         "properties": {
            "value": {
               "default": -1.7976931348623157e+308,
               "description": "Stopping criterion based on relative error",
               "title": "Value",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.convergence_tolerance",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  },
                  {
                     "ir_key": "method.jega.percent_change",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "convergence_tolerance_type": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodConvergenceTolWithTypeContext1Relative"
                  },
                  {
                     "$ref": "#/$defs/MethodConvergenceTolWithTypeContext1Absolute"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Convergence tolerance type",
               "title": "Convergence Tolerance Type",
               "x-union-pattern": 2
            }
         },
         "title": "MethodConvergenceTolWithTypeContext1ConvergenceTol",
         "type": "object"
      },
      "MethodConvergenceTolWithTypeContext1Relative": {
         "additionalProperties": false,
         "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark",
         "properties": {
            "relative": {
               "const": true,
               "default": true,
               "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark",
               "title": "Relative",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.convergence_tolerance_type",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "RELATIVE_CONVERGENCE_TOLERANCE"
                  }
               ]
            }
         },
         "title": "MethodConvergenceTolWithTypeContext1Relative",
         "type": "object"
      },
      "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,
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                     "stored_value": "STANDARD_MOMENTS"
                  }
               ]
            }
         },
         "title": "OnlinePilotFinalStatisticsQoiStatisticsFinalMomentsStandard",
         "type": "object"
      },
      "OnlinePilotRelaxationFactorSequence": {
         "additionalProperties": false,
         "description": "For under-relaxation of shared sample increments, apply a sequence of factors, one per iteration",
         "properties": {
            "factor_sequence": {
               "description": "For under-relaxation of shared sample increments, apply a sequence of factors, one per iteration",
               "items": {
                  "type": "number"
               },
               "title": "Factor Sequence",
               "type": "array",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.relaxation.factor_sequence",
                     "ir_value_type": "RealVector",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "required": [
            "factor_sequence"
         ],
         "title": "OnlinePilotRelaxationFactorSequence",
         "type": "object"
      },
      "OnlinePilotRelaxationFixedFactor": {
         "additionalProperties": false,
         "description": "For under-relaxation of shared sample increments, apply a fixed factor that is invariant with iteration",
         "properties": {
            "fixed_factor": {
               "description": "For under-relaxation of shared sample increments, apply a fixed factor that is invariant with iteration",
               "title": "Fixed Factor",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.relaxation.fixed_factor",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "required": [
            "fixed_factor"
         ],
         "title": "OnlinePilotRelaxationFixedFactor",
         "type": "object"
      },
      "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",
         "properties": {
            "recursive_factor": {
               "description": "For under-relaxation of shared sample increments, apply a recursive factor on each iteration that advances the relaxation factor toward 1",
               "title": "Recursive Factor",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.relaxation.recursive_factor",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "required": [
            "recursive_factor"
         ],
         "title": "OnlinePilotRelaxationRecursiveFactor",
         "type": "object"
      },
      "OnlineProjection": {
         "additionalProperties": false,
         "description": "Specify a solution mode that estimates performance based on projecting initial correlation / covariance estimates from an online pilot sample",
         "properties": {
            "online_projection": {
               "const": true,
               "default": true,
               "description": "Specify a solution mode that estimates performance based on projecting initial correlation / covariance estimates from an online pilot sample",
               "title": "Online Projection",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.ensemble_pilot_solution_mode",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "ONLINE_PILOT_PROJECTION"
                  }
               ]
            }
         },
         "title": "OnlineProjection",
         "type": "object"
      },
      "PromotedModelSelectionContext2SearchModelGraphs": {
         "additionalProperties": false,
         "description": "For weighted multilevel Monte Carlo, this option activates a search over possible hierarchical model graphs",
         "properties": {
            "model_selection": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Perform a recursion of admissible model subsets for a given model ensemble",
               "title": "Model Selection",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.selection",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "ALL_MODEL_COMBINATIONS"
                  }
               ]
            },
            "recursion_option": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/PromotedModelSelectionContext2SearchModelGraphsNoRecursion"
                  },
                  {
                     "$ref": "#/$defs/PromotedModelSelectionContext2SearchModelGraphsFullRecursion"
                  }
               ],
               "description": "DAG Ensemble Generation Option",
               "title": "Recursion Option",
               "x-union-pattern": 4
            }
         },
         "required": [
            "recursion_option"
         ],
         "title": "PromotedModelSelectionContext2SearchModelGraphs",
         "type": "object"
      },
      "PromotedModelSelectionContext2SearchModelGraphsFullRecursion": {
         "additionalProperties": false,
         "description": "Perform a full recursion of all admissible DAGs for a given model ensemble",
         "properties": {
            "full_recursion": {
               "const": true,
               "default": true,
               "description": "Perform a full recursion of all admissible DAGs for a given model ensemble",
               "title": "Full Recursion",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.recursion",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "FULL_GRAPH_RECURSION"
                  }
               ]
            }
         },
         "title": "PromotedModelSelectionContext2SearchModelGraphsFullRecursion",
         "type": "object"
      },
      "PromotedModelSelectionContext2SearchModelGraphsNoRecursion": {
         "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": "PromotedModelSelectionContext2SearchModelGraphsNoRecursion",
         "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"
      },
      "Scalarization": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to a mixture of terms of means and standard deviations.",
         "properties": {
            "scalarization": {
               "$ref": "#/$defs/ScalarizationConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TARGET_SCALARIZATION"
                  }
               ],
               "x-model-default": "ScalarizationConfig"
            }
         },
         "title": "Scalarization",
         "type": "object"
      },
      "ScalarizationConfig": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to a mixture of terms of means and standard deviations.",
         "properties": {
            "scalarization_response_mapping": {
               "anyOf": [
                  {
                     "items": {
                        "type": "number"
                     },
                     "type": "array"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Coefficients for linear scalarization (combination) of responses",
               "title": "Scalarization Response Mapping",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.scalarization_response_mapping",
                     "ir_value_type": "RealVector",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "optimization": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the scalarization.",
               "title": "Optimization",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target.optimization",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            }
         },
         "title": "ScalarizationConfig",
         "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"
      },
      "StandardDeviation": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.",
         "properties": {
            "standard_deviation": {
               "$ref": "#/$defs/StandardDeviationConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TARGET_SIGMA"
                  }
               ],
               "x-model-default": "StandardDeviationConfig"
            }
         },
         "title": "StandardDeviation",
         "type": "object"
      },
      "StandardDeviationConfig": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.",
         "properties": {
            "optimization": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the standard_deviation.",
               "title": "Optimization",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target.optimization",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            }
         },
         "title": "StandardDeviationConfig",
         "type": "object"
      },
      "Sum": {
         "additionalProperties": false,
         "description": "Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm.",
         "properties": {
            "sum": {
               "const": true,
               "default": true,
               "description": "Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm.",
               "title": "Sum",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.qoi_aggregation",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "QOI_AGGREGATION_SUM"
                  }
               ]
            }
         },
         "title": "Sum",
         "type": "object"
      },
      "Variance": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the variance.",
         "properties": {
            "variance": {
               "$ref": "#/$defs/VarianceConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "TARGET_VARIANCE"
                  }
               ],
               "x-model-default": "VarianceConfig"
            }
         },
         "title": "Variance",
         "type": "object"
      },
      "VarianceConfig": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the variance.",
         "properties": {
            "optimization": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the variance.",
               "title": "Optimization",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target.optimization",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            }
         },
         "title": "VarianceConfig",
         "type": "object"
      },
      "VarianceConstraint": {
         "additionalProperties": false,
         "description": "Allocate samples to target specified variance",
         "properties": {
            "variance_constraint": {
               "const": true,
               "default": true,
               "description": "Allocate samples to target specified variance",
               "title": "Variance Constraint",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.convergence_tolerance_target",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "VARIANCE_CONSTRAINT_TARGET"
                  }
               ]
            }
         },
         "title": "VarianceConstraint",
         "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"
      },
      "Weighted": {
         "additionalProperties": false,
         "description": "Include control variate weights for each of the recursive differences using in multilevel Monte Carlo (MLMC)",
         "properties": {
            "solver_metric": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodMlmfSolverMetricAverageEstimatorVariance"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSolverMetricNormEstimatorVariance"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSolverMetricMaxEstimatorVariance"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Metric employed during numerical solutions in sampling-based multifidelity UQ methods.",
               "title": "Solver Metric",
               "x-union-pattern": 2
            },
            "optimization_solver": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/MethodMlmfSubProblemSolverSqp"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSubProblemSolverNip"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSubProblemSolverGlobalLocal"
                  },
                  {
                     "$ref": "#/$defs/MethodMlmfSubProblemSolverCompetedLocal"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Optimization Solver",
               "title": "Optimization Solver",
               "x-union-pattern": 2
            },
            "search_model_graphs": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/PromotedModelSelectionContext2SearchModelGraphs"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "For weighted multilevel Monte Carlo, this option activates a search over possible hierarchical model graphs"
            }
         },
         "title": "Weighted",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "x-model-validations": [
      {
         "validationContext": "mlmfpilotsamplescontext1mixin",
         "validationErrorMessage": "For mlmfpilotsamplescontext1mixin, all elements of pilot_samples must be >= 0.",
         "validationFields": [
            "pilot_samples"
         ],
         "validationLiterals": [],
         "validationRuleName": "check_nonnegative_list"
      }
   ]
}

Fields:

• allocation_target (dakota.spec.method.multilevel_sampling.Mean | dakota.spec.method.multilevel_sampling.Variance | dakota.spec.method.multilevel_sampling.StandardDeviation | dakota.spec.method.multilevel_sampling.Scalarization)

• convergence_tolerance (dakota.spec.shared.core.MethodConvergenceTolWithTypeContext1ConvergenceTol | None)

• convergence_tolerance_target (dakota.spec.method.multilevel_sampling.VarianceConstraint | dakota.spec.method.multilevel_sampling.CostConstraint)

• export_sample_sequence (dakota.spec.method.multilevel_sampling.MultilevelSamplingExportSampleSequence | None)

• final_solutions (int)

• fixed_seed (Literal[True] | None)

• id_method (str | None)

• max_function_evaluations (int)

• max_iterations (int)

• model_pointer (str | 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)

• qoi_aggregation (dakota.spec.method.multilevel_sampling.Sum | dakota.spec.method.multilevel_sampling.Max)

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

• sample_type (dakota.spec.shared.sampling.MethodSampleTypeLhsMcLhs | dakota.spec.shared.sampling.MethodSampleTypeLhsMcRandom | 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)

• weighted (dakota.spec.method.multilevel_sampling.Weighted | None)

field allocation_target: Mean | Variance | StandardDeviation | Scalarization [Optional]

Allocation statistics/target for the MLMC sample allocation.

field convergence_tolerance: MethodConvergenceTolWithTypeContext1ConvergenceTol | None = None

Stopping criterion based on relative error

field convergence_tolerance_target: VarianceConstraint | CostConstraint [Optional]

Select target for MLMC sample allocation

field export_sample_sequence: MultilevelSamplingExportSampleSequence | 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

Stopping criterion based on number of refinement iterations within the multilevel sample allocation

Constraints:

• ge = 0

field model_pointer: str | None = None

Identifier for model block to be used by a method

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

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

field pilot_samples: list[int] | None = None

Initial set of samples for multilevel sampling methods.

field qoi_aggregation: Sum | Max [Optional]

Aggregation strategy for the QoIs statistics for problems with multiple responses in the MLMC algorithm

field rng: RngOptionsContext2Mt19937 | RngOptionsContext2Rnum2 [Optional]

Selection of a random number generator

field sample_type: MethodSampleTypeLhsMcLhs | MethodSampleTypeLhsMcRandom | None = None

Selection of sampling strategy

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

field weighted: Weighted | None = None

Include control variate weights for each of the recursive differences using in multilevel Monte Carlo (MLMC)

Generated Pydantic models for method.multilevel_sampling

pydantic model dakota.spec.method.multilevel_sampling.CostConstraint

Allocate samples to target specified cost

Show JSON schema

{
   "title": "CostConstraint",
   "description": "Allocate samples to target specified cost",
   "type": "object",
   "properties": {
      "cost_constraint": {
         "const": true,
         "default": true,
         "description": "Allocate samples to target specified cost",
         "title": "Cost Constraint",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.convergence_tolerance_target",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "COST_CONSTRAINT_TARGET"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• cost_constraint (Literal[True])

field cost_constraint: Literal[True] = True

Allocate samples to target specified cost

pydantic model dakota.spec.method.multilevel_sampling.Max

Compute sample allocation for each response and use maximum over responses for each level in a MLMC algorithm

Show JSON schema

{
   "title": "Max",
   "description": "Compute sample allocation for each response and use maximum over responses for each level in a MLMC algorithm",
   "type": "object",
   "properties": {
      "max": {
         "const": true,
         "default": true,
         "description": "Compute sample allocation for each response and use maximum over responses for each level in a MLMC algorithm",
         "title": "Max",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.qoi_aggregation",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "QOI_AGGREGATION_MAX"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• max (Literal[True])

field max: Literal[True] = True

Compute sample allocation for each response and use maximum over responses for each level in a MLMC algorithm

pydantic model dakota.spec.method.multilevel_sampling.Mean

Fit MLMC sample allocation to control the variance of the estimator for the mean.

Show JSON schema

{
   "title": "Mean",
   "description": "Fit MLMC sample allocation to control the variance of the estimator for the mean.",
   "type": "object",
   "properties": {
      "mean": {
         "const": true,
         "default": true,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the mean.",
         "title": "Mean",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.allocation_target",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "TARGET_MEAN"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• mean (Literal[True])

field mean: Literal[True] = True

Fit MLMC sample allocation to control the variance of the estimator for the mean.

pydantic model dakota.spec.method.multilevel_sampling.MultilevelSamplingExportSampleSequence

Enable export of multilevel/multifidelity sample sequences to individual files

Show JSON schema

{
   "title": "MultilevelSamplingExportSampleSequence",
   "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

pydantic model dakota.spec.method.multilevel_sampling.Scalarization

Fit MLMC sample allocation to a mixture of terms of means and standard deviations.

Show JSON schema

{
   "title": "Scalarization",
   "description": "Fit MLMC sample allocation to a mixture of terms of means and standard deviations.",
   "type": "object",
   "properties": {
      "scalarization": {
         "$ref": "#/$defs/ScalarizationConfig",
         "x-materialization": [
            {
               "ir_key": "method.nond.allocation_target",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "TARGET_SCALARIZATION"
            }
         ],
         "x-model-default": "ScalarizationConfig"
      }
   },
   "$defs": {
      "ScalarizationConfig": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to a mixture of terms of means and standard deviations.",
         "properties": {
            "scalarization_response_mapping": {
               "anyOf": [
                  {
                     "items": {
                        "type": "number"
                     },
                     "type": "array"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Coefficients for linear scalarization (combination) of responses",
               "title": "Scalarization Response Mapping",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.scalarization_response_mapping",
                     "ir_value_type": "RealVector",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            },
            "optimization": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the scalarization.",
               "title": "Optimization",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target.optimization",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            }
         },
         "title": "ScalarizationConfig",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Fields:

• scalarization (dakota.spec.method.multilevel_sampling.ScalarizationConfig)

field scalarization: ScalarizationConfig [Optional]

Fit MLMC sample allocation to a mixture of terms of means and standard deviations.

pydantic model dakota.spec.method.multilevel_sampling.ScalarizationConfig

Fit MLMC sample allocation to a mixture of terms of means and standard deviations.

Show JSON schema

{
   "title": "ScalarizationConfig",
   "description": "Fit MLMC sample allocation to a mixture of terms of means and standard deviations.",
   "type": "object",
   "properties": {
      "scalarization_response_mapping": {
         "anyOf": [
            {
               "items": {
                  "type": "number"
               },
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Coefficients for linear scalarization (combination) of responses",
         "title": "Scalarization Response Mapping",
         "x-materialization": [
            {
               "ir_key": "method.nond.scalarization_response_mapping",
               "ir_value_type": "RealVector",
               "storage_type": "DIRECT_VALUE"
            }
         ]
      },
      "optimization": {
         "anyOf": [
            {
               "const": true,
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the scalarization.",
         "title": "Optimization",
         "x-materialization": [
            {
               "ir_key": "method.nond.allocation_target.optimization",
               "ir_value_type": "bool",
               "storage_type": "PRESENCE_TRUE"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• optimization (Literal[True] | None)

• scalarization_response_mapping (list[float] | None)

field optimization: Literal[True] | None = None

Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the scalarization.

field scalarization_response_mapping: list[DakotaFloat] | None = None

Coefficients for linear scalarization (combination) of responses

pydantic model dakota.spec.method.multilevel_sampling.StandardDeviation

Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.

Show JSON schema

{
   "title": "StandardDeviation",
   "description": "Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.",
   "type": "object",
   "properties": {
      "standard_deviation": {
         "$ref": "#/$defs/StandardDeviationConfig",
         "x-materialization": [
            {
               "ir_key": "method.nond.allocation_target",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "TARGET_SIGMA"
            }
         ],
         "x-model-default": "StandardDeviationConfig"
      }
   },
   "$defs": {
      "StandardDeviationConfig": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.",
         "properties": {
            "optimization": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the standard_deviation.",
               "title": "Optimization",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target.optimization",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            }
         },
         "title": "StandardDeviationConfig",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Fields:

• standard_deviation (dakota.spec.method.multilevel_sampling.StandardDeviationConfig)

field standard_deviation: StandardDeviationConfig [Optional]

Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.

pydantic model dakota.spec.method.multilevel_sampling.StandardDeviationConfig

Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.

Show JSON schema

{
   "title": "StandardDeviationConfig",
   "description": "Fit MLMC sample allocation to control the variance of the estimator for the standard deviation.",
   "type": "object",
   "properties": {
      "optimization": {
         "anyOf": [
            {
               "const": true,
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the standard_deviation.",
         "title": "Optimization",
         "x-materialization": [
            {
               "ir_key": "method.nond.allocation_target.optimization",
               "ir_value_type": "bool",
               "storage_type": "PRESENCE_TRUE"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• optimization (Literal[True] | None)

field optimization: Literal[True] | None = None

Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the standard_deviation.

pydantic model dakota.spec.method.multilevel_sampling.Sum

Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm.

Show JSON schema

{
   "title": "Sum",
   "description": "Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm.",
   "type": "object",
   "properties": {
      "sum": {
         "const": true,
         "default": true,
         "description": "Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm.",
         "title": "Sum",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.qoi_aggregation",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "QOI_AGGREGATION_SUM"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• sum (Literal[True])

field sum: Literal[True] = True

Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm.

pydantic model dakota.spec.method.multilevel_sampling.Variance

Fit MLMC sample allocation to control the variance of the estimator for the variance.

Show JSON schema

{
   "title": "Variance",
   "description": "Fit MLMC sample allocation to control the variance of the estimator for the variance.",
   "type": "object",
   "properties": {
      "variance": {
         "$ref": "#/$defs/VarianceConfig",
         "x-materialization": [
            {
               "ir_key": "method.nond.allocation_target",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "TARGET_VARIANCE"
            }
         ],
         "x-model-default": "VarianceConfig"
      }
   },
   "$defs": {
      "VarianceConfig": {
         "additionalProperties": false,
         "description": "Fit MLMC sample allocation to control the variance of the estimator for the variance.",
         "properties": {
            "optimization": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the variance.",
               "title": "Optimization",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.allocation_target.optimization",
                     "ir_value_type": "bool",
                     "storage_type": "PRESENCE_TRUE"
                  }
               ]
            }
         },
         "title": "VarianceConfig",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Fields:

• variance (dakota.spec.method.multilevel_sampling.VarianceConfig)

field variance: VarianceConfig [Optional]

Fit MLMC sample allocation to control the variance of the estimator for the variance.

pydantic model dakota.spec.method.multilevel_sampling.VarianceConfig

Fit MLMC sample allocation to control the variance of the estimator for the variance.

Show JSON schema

{
   "title": "VarianceConfig",
   "description": "Fit MLMC sample allocation to control the variance of the estimator for the variance.",
   "type": "object",
   "properties": {
      "optimization": {
         "anyOf": [
            {
               "const": true,
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the variance.",
         "title": "Optimization",
         "x-materialization": [
            {
               "ir_key": "method.nond.allocation_target.optimization",
               "ir_value_type": "bool",
               "storage_type": "PRESENCE_TRUE"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• optimization (Literal[True] | None)

field optimization: Literal[True] | None = None

Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the variance.

pydantic model dakota.spec.method.multilevel_sampling.VarianceConstraint

Allocate samples to target specified variance

Show JSON schema

{
   "title": "VarianceConstraint",
   "description": "Allocate samples to target specified variance",
   "type": "object",
   "properties": {
      "variance_constraint": {
         "const": true,
         "default": true,
         "description": "Allocate samples to target specified variance",
         "title": "Variance Constraint",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.convergence_tolerance_target",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "VARIANCE_CONSTRAINT_TARGET"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• variance_constraint (Literal[True])

field variance_constraint: Literal[True] = True

Allocate samples to target specified variance

pydantic model dakota.spec.method.multilevel_sampling.Weighted

Include control variate weights for each of the recursive differences using in multilevel Monte Carlo (MLMC)

Show JSON schema

{
   "title": "Weighted",
   "description": "Include control variate weights for each of the recursive differences using in multilevel Monte Carlo (MLMC)",
   "type": "object",
   "properties": {
      "solver_metric": {
         "anyOf": [
            {
               "$ref": "#/$defs/MethodMlmfSolverMetricAverageEstimatorVariance"
            },
            {
               "$ref": "#/$defs/MethodMlmfSolverMetricNormEstimatorVariance"
            },
            {
               "$ref": "#/$defs/MethodMlmfSolverMetricMaxEstimatorVariance"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Metric employed during numerical solutions in sampling-based multifidelity UQ methods.",
         "title": "Solver Metric",
         "x-union-pattern": 2
      },
      "optimization_solver": {
         "anchor": true,
         "anyOf": [
            {
               "$ref": "#/$defs/MethodMlmfSubProblemSolverSqp"
            },
            {
               "$ref": "#/$defs/MethodMlmfSubProblemSolverNip"
            },
            {
               "$ref": "#/$defs/MethodMlmfSubProblemSolverGlobalLocal"
            },
            {
               "$ref": "#/$defs/MethodMlmfSubProblemSolverCompetedLocal"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Optimization Solver",
         "title": "Optimization Solver",
         "x-union-pattern": 2
      },
      "search_model_graphs": {
         "anyOf": [
            {
               "$ref": "#/$defs/PromotedModelSelectionContext2SearchModelGraphs"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "For weighted multilevel Monte Carlo, this option activates a search over possible hierarchical model graphs"
      }
   },
   "$defs": {
      "MethodMlmfSolverMetricAverageEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "average_estimator_variance": {
               "const": true,
               "default": true,
               "description": "Utilize the estimator variance averaged over the QoI as the solver metric for sampling-based multifidelity methods.",
               "title": "Average Estimator Variance",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "AVG_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "title": "MethodMlmfSolverMetricAverageEstimatorVariance",
         "type": "object"
      },
      "MethodMlmfSolverMetricMaxEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "max_estimator_variance": {
               "const": true,
               "default": true,
               "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
               "title": "Max Estimator Variance",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "MAX_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "title": "MethodMlmfSolverMetricMaxEstimatorVariance",
         "type": "object"
      },
      "MethodMlmfSolverMetricNormEstimatorVariance": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "norm_estimator_variance": {
               "$ref": "#/$defs/MethodMlmfSolverMetricNormEstimatorVarianceConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "NORM_ESTVAR_METRIC"
                  }
               ]
            }
         },
         "required": [
            "norm_estimator_variance"
         ],
         "title": "MethodMlmfSolverMetricNormEstimatorVariance",
         "type": "object"
      },
      "MethodMlmfSolverMetricNormEstimatorVarianceConfig": {
         "additionalProperties": false,
         "description": "Utilize a p-norm over the vector of QoI estimator variances as the solver metric for sampling-based multifidelity methods.",
         "properties": {
            "norm_order": {
               "default": 2.0,
               "description": "Utilize the response covariance metric for guiding adaptive refinement during UQ.",
               "minimum": 1.0,
               "title": "Norm Order",
               "type": "number",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.estimator_variance_metric_norm_order",
                     "ir_value_type": "Real",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "title": "MethodMlmfSolverMetricNormEstimatorVarianceConfig",
         "type": "object"
      },
      "MethodMlmfSubProblemSolverCompetedLocal": {
         "additionalProperties": false,
         "description": "Use a competed local solver scheme for solving an optimization sub-problem",
         "properties": {
            "competed_local": {
               "const": true,
               "default": true,
               "description": "Use a competed local solver scheme for solving an optimization sub-problem",
               "title": "Competed Local",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_NPSOL_OPTPP"
                  }
               ]
            }
         },
         "title": "MethodMlmfSubProblemSolverCompetedLocal",
         "type": "object"
      },
      "MethodMlmfSubProblemSolverGlobalLocal": {
         "additionalProperties": false,
         "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
         "properties": {
            "global_local": {
               "const": true,
               "default": true,
               "description": "Use a hybrid global-local scheme for solving an optimization sub-problem",
               "title": "Global Local",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_DIRECT_NPSOL_OPTPP"
                  }
               ]
            }
         },
         "title": "MethodMlmfSubProblemSolverGlobalLocal",
         "type": "object"
      },
      "MethodMlmfSubProblemSolverNip": {
         "additionalProperties": false,
         "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
         "properties": {
            "nip": {
               "const": true,
               "default": true,
               "description": "Use a nonlinear interior point method for solving an optimization sub-problem",
               "title": "Nip",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_OPTPP"
                  }
               ]
            }
         },
         "title": "MethodMlmfSubProblemSolverNip",
         "type": "object"
      },
      "MethodMlmfSubProblemSolverSqp": {
         "additionalProperties": false,
         "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
         "properties": {
            "sqp": {
               "const": true,
               "default": true,
               "description": "Use a sequential quadratic programming method for solving an optimization sub-problem",
               "title": "Sqp",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.opt_subproblem_solver",
                     "ir_value_type": "unsigned short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SUBMETHOD_NPSOL"
                  }
               ]
            }
         },
         "title": "MethodMlmfSubProblemSolverSqp",
         "type": "object"
      },
      "PromotedModelSelectionContext2SearchModelGraphs": {
         "additionalProperties": false,
         "description": "For weighted multilevel Monte Carlo, this option activates a search over possible hierarchical model graphs",
         "properties": {
            "model_selection": {
               "anyOf": [
                  {
                     "const": true,
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Perform a recursion of admissible model subsets for a given model ensemble",
               "title": "Model Selection",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.selection",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "ALL_MODEL_COMBINATIONS"
                  }
               ]
            },
            "recursion_option": {
               "anchor": true,
               "anyOf": [
                  {
                     "$ref": "#/$defs/PromotedModelSelectionContext2SearchModelGraphsNoRecursion"
                  },
                  {
                     "$ref": "#/$defs/PromotedModelSelectionContext2SearchModelGraphsFullRecursion"
                  }
               ],
               "description": "DAG Ensemble Generation Option",
               "title": "Recursion Option",
               "x-union-pattern": 4
            }
         },
         "required": [
            "recursion_option"
         ],
         "title": "PromotedModelSelectionContext2SearchModelGraphs",
         "type": "object"
      },
      "PromotedModelSelectionContext2SearchModelGraphsFullRecursion": {
         "additionalProperties": false,
         "description": "Perform a full recursion of all admissible DAGs for a given model ensemble",
         "properties": {
            "full_recursion": {
               "const": true,
               "default": true,
               "description": "Perform a full recursion of all admissible DAGs for a given model ensemble",
               "title": "Full Recursion",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.recursion",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "FULL_GRAPH_RECURSION"
                  }
               ]
            }
         },
         "title": "PromotedModelSelectionContext2SearchModelGraphsFullRecursion",
         "type": "object"
      },
      "PromotedModelSelectionContext2SearchModelGraphsNoRecursion": {
         "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": "PromotedModelSelectionContext2SearchModelGraphsNoRecursion",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Fields:

• optimization_solver (dakota.spec.shared.misc.MethodMlmfSubProblemSolverSqp | dakota.spec.shared.misc.MethodMlmfSubProblemSolverNip | dakota.spec.shared.misc.MethodMlmfSubProblemSolverGlobalLocal | dakota.spec.shared.misc.MethodMlmfSubProblemSolverCompetedLocal | None)

• search_model_graphs (dakota.spec.shared.misc.PromotedModelSelectionContext2SearchModelGraphs | None)

• solver_metric (dakota.spec.shared.misc.MethodMlmfSolverMetricAverageEstimatorVariance | dakota.spec.shared.misc.MethodMlmfSolverMetricNormEstimatorVariance | dakota.spec.shared.misc.MethodMlmfSolverMetricMaxEstimatorVariance | None)

field optimization_solver: MethodMlmfSubProblemSolverSqp | MethodMlmfSubProblemSolverNip | MethodMlmfSubProblemSolverGlobalLocal | MethodMlmfSubProblemSolverCompetedLocal | None = None

Optimization Solver

field search_model_graphs: PromotedModelSelectionContext2SearchModelGraphs | None = None

For weighted multilevel Monte Carlo, this option activates a search over possible hierarchical model graphs

field solver_metric: MethodMlmfSolverMetricAverageEstimatorVariance | MethodMlmfSolverMetricNormEstimatorVariance | MethodMlmfSolverMetricMaxEstimatorVariance | None = None

Metric employed during numerical solutions in sampling-based multifidelity UQ methods.