approximate_control_variate

pydantic model dakota.spec.method.approximate_control_variate.ApproximateControlVariateSelection

Generated model for ApproximateControlVariateSelection

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{
   "title": "ApproximateControlVariateSelection",
   "description": "Generated model for ApproximateControlVariateSelection",
   "type": "object",
   "properties": {
      "approximate_control_variate": {
         "$ref": "#/$defs/ApproximateControlVariateConfig",
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            "acv_sampling"
         ],
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               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "APPROX_CONTROL_VARIATE"
            }
         ]
      }
   },
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         "properties": {
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               "default": true,
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               "title": "Acv Independent Sampling",
               "type": "boolean",
               "x-aliases": [
                  "acv_is"
               ],
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                  }
               ]
            }
         },
         "title": "AcvIndependentSampling",
         "type": "object"
      },
      "AcvMultifidelity": {
         "additionalProperties": false,
         "description": "Approximate control variate (ACV) algorithm that employs a nested pyramid sample pattern, similar to MFMC",
         "properties": {
            "acv_multifidelity": {
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               "default": true,
               "description": "Approximate control variate (ACV) algorithm that employs a nested pyramid sample pattern, similar to MFMC",
               "title": "Acv Multifidelity",
               "type": "boolean",
               "x-aliases": [
                  "acv_mf"
               ],
               "x-materialization": [
                  {
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                  }
               ]
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         },
         "title": "AcvMultifidelity",
         "type": "object"
      },
      "AcvRecursiveDiff": {
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         "description": "Sampling scheme within the approximate control variate (ACV) algorithm that employs weighted recursive differences (RD) across model pairings",
         "properties": {
            "acv_recursive_diff": {
               "const": true,
               "default": true,
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               "title": "Acv Recursive Diff",
               "type": "boolean",
               "x-aliases": [
                  "acv_rd"
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                  }
               ]
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         },
         "title": "AcvRecursiveDiff",
         "type": "object"
      },
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         "description": "Approximate control variate (ACV) sampling methods for UQ",
         "properties": {
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               "title": "Model Pointer",
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               ]
            },
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                     "$ref": "#/$defs/RngOptionsContext2Rnum2"
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               ],
               "description": "Selection of a random number generator",
               "title": "Rng",
               "x-model-default": "RngOptionsContext2Mt19937",
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            },
            "max_function_evaluations": {
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            },
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               ],
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            },
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                     "$ref": "#/$defs/MethodSampleTypeLhsMcLhs"
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            },
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               ]
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                  },
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                     "$ref": "#/$defs/MethodMlmfSolverMetricNormEstimatorVariance"
                  },
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                  },
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            },
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                     "$ref": "#/$defs/OfflineProjection"
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               ],
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               "title": "Solution Mode",
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            },
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               ],
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            },
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                     "type": "string"
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               ],
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               ]
            },
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                  },
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                  },
                  {
                     "$ref": "#/$defs/Quiet"
                  },
                  {
                     "$ref": "#/$defs/Silent"
                  }
               ],
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               "title": "Output",
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            },
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                     "$ref": "#/$defs/AcvIndependentSampling"
                  },
                  {
                     "$ref": "#/$defs/AcvMultifidelity"
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                     "$ref": "#/$defs/AcvRecursiveDiff"
                  }
               ],
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               "title": "Solution Approach",
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            },
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                  {
                     "$ref": "#/$defs/ApproximateControlVariateSearchModelGraphs"
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                     "type": "null"
                  }
               ],
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            },
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               ],
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               ]
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                     "$ref": "#/$defs/ApproximateControlVariateExportSampleSequence"
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         },
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            "solution_approach"
         ],
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               "validationErrorMessage": "For methodseedsequencemixin, all elements of seed_sequence must be >= 0.",
               "validationFields": [
                  "seed_sequence"
               ],
               "validationLiterals": [],
               "validationRuleName": "check_nonnegative_list"
            }
         ]
      },
      "ApproximateControlVariateExportSampleSequence": {
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         "properties": {
            "format": {
               "anchor": true,
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                     "$ref": "#/$defs/MethodExportSamplesFormatCustomAnnotated"
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                     "$ref": "#/$defs/MethodExportSamplesFormatAnnotated"
                  },
                  {
                     "$ref": "#/$defs/MethodExportSamplesFormatFreeform"
                  }
               ],
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               "title": "Format",
               "x-model-default": "MethodExportSamplesFormatAnnotated",
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            }
         },
         "title": "ApproximateControlVariateExportSampleSequence",
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      },
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         "properties": {
            "model_selection": {
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                  {
                     "const": true,
                     "type": "boolean"
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                  {
                     "type": "null"
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               ],
               "default": null,
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               ]
            },
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               "anchor": true,
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                     "$ref": "#/$defs/ApproximateControlVariateSearchModelGraphsNoRecursion"
                  },
                  {
                     "$ref": "#/$defs/KlRecursion"
                  },
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                     "$ref": "#/$defs/PartialRecursion"
                  },
                  {
                     "$ref": "#/$defs/ApproximateControlVariateSearchModelGraphsFullRecursion"
                  }
               ],
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               "title": "Dag Recursion",
               "x-union-pattern": 4
            }
         },
         "required": [
            "dag_recursion"
         ],
         "title": "ApproximateControlVariateSearchModelGraphs",
         "type": "object"
      },
      "ApproximateControlVariateSearchModelGraphsFullRecursion": {
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         "description": "Perform a full recursion of all admissible DAGs for a given model ensemble",
         "properties": {
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               "default": true,
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         },
         "title": "ApproximateControlVariateSearchModelGraphsFullRecursion",
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         "description": "Do not recur over admissible DAGs for a given model ensemble",
         "properties": {
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               "const": true,
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               "description": "Do not recur over admissible DAGs for a given model ensemble",
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         },
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         "description": "Level 5 of 5 - maximum",
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               ]
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         },
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         "description": "Model graph enumeration that follows the ACV-KL partitioning scheme",
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         "description": "Utilize the maximum estimator variance as the solver metric for sampling-based multifidelity methods.",
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         "description": "Specify a solution mode that includes the pilot cost within the sample allocation logic",
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         "description": "Return estimator performance as the final results of a UQ method",
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         },
         "required": [
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         ],
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      },
      "PartialRecursionConfig": {
         "additionalProperties": false,
         "description": "Perform a partial recursion of admissible DAGs",
         "properties": {
            "depth_limit": {
               "description": "Throttle the depth for a partial recursion of admissible DAGs",
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         "description": "Level 2 of 5 - less than normal",
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               ]
            }
         },
         "title": "RngOptionsContext2Mt19937",
         "type": "object"
      },
      "RngOptionsContext2Rnum2": {
         "additionalProperties": false,
         "description": "Generates pseudo-random numbers using the Pecos package",
         "properties": {
            "rnum2": {
               "const": true,
               "default": true,
               "description": "Generates pseudo-random numbers using the Pecos package",
               "title": "Rnum2",
               "type": "boolean",
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               ]
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         "title": "RngOptionsContext2Rnum2",
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      },
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         "additionalProperties": false,
         "description": "Level 1 of 5 - minimum",
         "properties": {
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               "const": true,
               "default": true,
               "description": "Level 1 of 5 - minimum",
               "title": "Silent",
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         "title": "Silent",
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         "properties": {
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               "default": true,
               "description": "Level 4 of 5 - more than normal",
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               "type": "boolean",
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         "title": "Verbose",
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   },
   "additionalProperties": false,
   "required": [
      "approximate_control_variate"
   ]
}

Fields:

• approximate_control_variate (dakota.spec.method.approximate_control_variate.ApproximateControlVariateConfig)

field approximate_control_variate: ApproximateControlVariateConfig [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.approximate_control_variate.ApproximateControlVariateConfig

Approximate control variate (ACV) sampling methods for UQ

Show JSON schema

{
   "title": "ApproximateControlVariateConfig",
   "description": "Approximate control variate (ACV) sampling methods for UQ",
   "type": "object",
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      },
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               "$ref": "#/$defs/MethodSampleTypeLhsMcLhs"
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               "$ref": "#/$defs/OfflineProjection"
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                     "$ref": "#/$defs/MethodExportSamplesFormatFreeform"
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               ],
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         },
         "title": "ApproximateControlVariateExportSampleSequence",
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         "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"
      },
      "PartialRecursion": {
         "additionalProperties": false,
         "description": "Perform a partial recursion of admissible DAGs",
         "properties": {
            "partial_recursion": {
               "$ref": "#/$defs/PartialRecursionConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.recursion",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "PARTIAL_GRAPH_RECURSION"
                  }
               ]
            }
         },
         "required": [
            "partial_recursion"
         ],
         "title": "PartialRecursion",
         "type": "object"
      },
      "PartialRecursionConfig": {
         "additionalProperties": false,
         "description": "Perform a partial recursion of admissible DAGs",
         "properties": {
            "depth_limit": {
               "description": "Throttle the depth for a partial recursion of admissible DAGs",
               "title": "Depth Limit",
               "type": "integer",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.graph_depth_limit",
                     "ir_value_type": "unsigned short",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "required": [
            "depth_limit"
         ],
         "title": "PartialRecursionConfig",
         "type": "object"
      },
      "Quiet": {
         "additionalProperties": false,
         "description": "Level 2 of 5 - less than normal",
         "properties": {
            "quiet": {
               "const": true,
               "default": true,
               "description": "Level 2 of 5 - less than normal",
               "title": "Quiet",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "QUIET_OUTPUT"
                  }
               ]
            }
         },
         "title": "Quiet",
         "type": "object"
      },
      "RngOptionsContext2Mt19937": {
         "additionalProperties": false,
         "description": "Generates random numbers using the Mersenne twister",
         "properties": {
            "mt19937": {
               "const": true,
               "default": true,
               "description": "Generates random numbers using the Mersenne twister",
               "title": "Mt19937",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.random_number_generator",
                     "ir_value_type": "String",
                     "storage_type": "PRESENCE_LITERAL",
                     "stored_value": "mt19937"
                  }
               ]
            }
         },
         "title": "RngOptionsContext2Mt19937",
         "type": "object"
      },
      "RngOptionsContext2Rnum2": {
         "additionalProperties": false,
         "description": "Generates pseudo-random numbers using the Pecos package",
         "properties": {
            "rnum2": {
               "const": true,
               "default": true,
               "description": "Generates pseudo-random numbers using the Pecos package",
               "title": "Rnum2",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.random_number_generator",
                     "ir_value_type": "String",
                     "storage_type": "PRESENCE_LITERAL",
                     "stored_value": "rnum2"
                  }
               ]
            }
         },
         "title": "RngOptionsContext2Rnum2",
         "type": "object"
      },
      "Silent": {
         "additionalProperties": false,
         "description": "Level 1 of 5 - minimum",
         "properties": {
            "silent": {
               "const": true,
               "default": true,
               "description": "Level 1 of 5 - minimum",
               "title": "Silent",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "SILENT_OUTPUT"
                  }
               ]
            }
         },
         "title": "Silent",
         "type": "object"
      },
      "Verbose": {
         "additionalProperties": false,
         "description": "Level 4 of 5 - more than normal",
         "properties": {
            "verbose": {
               "const": true,
               "default": true,
               "description": "Level 4 of 5 - more than normal",
               "title": "Verbose",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.output",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "VERBOSE_OUTPUT"
                  }
               ]
            }
         },
         "title": "Verbose",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "required": [
      "solution_approach"
   ],
   "x-model-validations": [
      {
         "validationContext": "methodseedsequencemixin",
         "validationErrorMessage": "For methodseedsequencemixin, all elements of seed_sequence must be >= 0.",
         "validationFields": [
            "seed_sequence"
         ],
         "validationLiterals": [],
         "validationRuleName": "check_nonnegative_list"
      }
   ]
}

Fields:

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

• export_sample_sequence (dakota.spec.method.approximate_control_variate.ApproximateControlVariateExportSampleSequence | None)

• final_solutions (int)

• fixed_seed (Literal[True] | None)

• id_method (str | None)

• max_function_evaluations (int)

• max_iterations (int)

• model_pointer (str | None)

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

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

• pilot_samples (list[int] | None)

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

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

• search_model_graphs (dakota.spec.method.approximate_control_variate.ApproximateControlVariateSearchModelGraphs | None)

• seed_sequence (list[int] | None)

• solution_approach (dakota.spec.method.approximate_control_variate.AcvIndependentSampling | dakota.spec.method.approximate_control_variate.AcvMultifidelity | dakota.spec.method.approximate_control_variate.AcvRecursiveDiff)

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

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

• truth_fixed_by_pilot (Literal[True] | None)

field convergence_tolerance: MethodConvergenceTolWithTypeContext3ConvergenceTol | None = None

Stopping criterion based on relative error reduction

field export_sample_sequence: ApproximateControlVariateExportSampleSequence | None = None

Enable export of multilevel/multifidelity sample sequences to individual files

field final_solutions: int = 0

Number of designs returned as the best solutions

Constraints:

• ge = 0

field fixed_seed: Literal[True] | None = None

Reuses the same seed value for multiple random sampling sets

field id_method: str | None = None

Name the method block; helpful when there are multiple

field max_function_evaluations: int = 9223372036854775807

Stopping criterion based on maximum function evaluations

Constraints:

• ge = 0

field max_iterations: int = 9223372036854775807

Number of iterations allowed for optimizers and adaptive UQ methods

Constraints:

• ge = 0

field model_pointer: str | None = None

Identifier for model block to be used by a method

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

Optimization Solver

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

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

field pilot_samples: list[int] | None = None

Initial set of samples for multilevel/multifidelity sampling methods.

field rng: RngOptionsContext2Mt19937 | RngOptionsContext2Rnum2 [Optional]

Selection of a random number generator

field sample_type: MethodSampleTypeLhsMcLhs | MethodSampleTypeLhsMcRandom | None = None

Selection of sampling strategy

field search_model_graphs: ApproximateControlVariateSearchModelGraphs | None = None

Perform a recursion of admissible DAGs for a given model ensemble

field seed_sequence: list[int] | None = None

Sequence of seed values for multi-stage random sampling

field solution_approach: AcvIndependentSampling | AcvMultifidelity | AcvRecursiveDiff [Required]

Solution Approach

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

Solution mode for multilevel/multifidelity methods

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

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

field truth_fixed_by_pilot: Literal[True] | None = None

Option to suppress any increment to the number of truth samples

Generated Pydantic models for method.approximate_control_variate

pydantic model dakota.spec.method.approximate_control_variate.AcvIndependentSampling

Sampling scheme within the approximate control variate (ACV) algorithm that employs independent samples (IS) across model pairings

Show JSON schema

{
   "title": "AcvIndependentSampling",
   "description": "Sampling scheme within the approximate control variate (ACV) algorithm that employs independent samples (IS) across model pairings",
   "type": "object",
   "properties": {
      "acv_independent_sampling": {
         "const": true,
         "default": true,
         "description": "Sampling scheme within the approximate control variate (ACV) algorithm that employs independent samples (IS) across model pairings",
         "title": "Acv Independent Sampling",
         "type": "boolean",
         "x-aliases": [
            "acv_is"
         ],
         "x-materialization": [
            {
               "ir_key": "method.sub_method",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "SUBMETHOD_ACV_IS"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• acv_independent_sampling (Literal[True])

field acv_independent_sampling: Literal[True] = True

Sampling scheme within the approximate control variate (ACV) algorithm that employs independent samples (IS) across model pairings

pydantic model dakota.spec.method.approximate_control_variate.AcvMultifidelity

Approximate control variate (ACV) algorithm that employs a nested pyramid sample pattern, similar to MFMC

Show JSON schema

{
   "title": "AcvMultifidelity",
   "description": "Approximate control variate (ACV) algorithm that employs a nested pyramid sample pattern, similar to MFMC",
   "type": "object",
   "properties": {
      "acv_multifidelity": {
         "const": true,
         "default": true,
         "description": "Approximate control variate (ACV) algorithm that employs a nested pyramid sample pattern, similar to MFMC",
         "title": "Acv Multifidelity",
         "type": "boolean",
         "x-aliases": [
            "acv_mf"
         ],
         "x-materialization": [
            {
               "ir_key": "method.sub_method",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "SUBMETHOD_ACV_MF"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• acv_multifidelity (Literal[True])

field acv_multifidelity: Literal[True] = True

Approximate control variate (ACV) algorithm that employs a nested pyramid sample pattern, similar to MFMC

pydantic model dakota.spec.method.approximate_control_variate.AcvRecursiveDiff

Sampling scheme within the approximate control variate (ACV) algorithm that employs weighted recursive differences (RD) across model pairings

Show JSON schema

{
   "title": "AcvRecursiveDiff",
   "description": "Sampling scheme within the approximate control variate (ACV) algorithm that employs weighted recursive differences (RD) across model pairings",
   "type": "object",
   "properties": {
      "acv_recursive_diff": {
         "const": true,
         "default": true,
         "description": "Sampling scheme within the approximate control variate (ACV) algorithm that employs weighted recursive differences (RD) across model pairings",
         "title": "Acv Recursive Diff",
         "type": "boolean",
         "x-aliases": [
            "acv_rd"
         ],
         "x-materialization": [
            {
               "ir_key": "method.sub_method",
               "ir_value_type": "unsigned short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "SUBMETHOD_ACV_RD"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• acv_recursive_diff (Literal[True])

field acv_recursive_diff: Literal[True] = True

Sampling scheme within the approximate control variate (ACV) algorithm that employs weighted recursive differences (RD) across model pairings

pydantic model dakota.spec.method.approximate_control_variate.ApproximateControlVariateExportSampleSequence

Enable export of multilevel/multifidelity sample sequences to individual files

Show JSON schema

{
   "title": "ApproximateControlVariateExportSampleSequence",
   "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.approximate_control_variate.ApproximateControlVariateSearchModelGraphs

Perform a recursion of admissible DAGs for a given model ensemble

Show JSON schema

{
   "title": "ApproximateControlVariateSearchModelGraphs",
   "description": "Perform a recursion of admissible DAGs for a given model ensemble",
   "type": "object",
   "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"
            }
         ]
      },
      "dag_recursion": {
         "anchor": true,
         "anyOf": [
            {
               "$ref": "#/$defs/ApproximateControlVariateSearchModelGraphsNoRecursion"
            },
            {
               "$ref": "#/$defs/KlRecursion"
            },
            {
               "$ref": "#/$defs/PartialRecursion"
            },
            {
               "$ref": "#/$defs/ApproximateControlVariateSearchModelGraphsFullRecursion"
            }
         ],
         "description": "DAG Ensemble Generation Option",
         "title": "Dag Recursion",
         "x-union-pattern": 4
      }
   },
   "$defs": {
      "ApproximateControlVariateSearchModelGraphsFullRecursion": {
         "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": "ApproximateControlVariateSearchModelGraphsFullRecursion",
         "type": "object"
      },
      "ApproximateControlVariateSearchModelGraphsNoRecursion": {
         "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": "ApproximateControlVariateSearchModelGraphsNoRecursion",
         "type": "object"
      },
      "KlRecursion": {
         "additionalProperties": false,
         "description": "Model graph enumeration that follows the ACV-KL partitioning scheme",
         "properties": {
            "kl_recursion": {
               "const": true,
               "default": true,
               "description": "Model graph enumeration that follows the ACV-KL partitioning scheme",
               "title": "Kl Recursion",
               "type": "boolean",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.recursion",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "KL_GRAPH_RECURSION"
                  }
               ]
            }
         },
         "title": "KlRecursion",
         "type": "object"
      },
      "PartialRecursion": {
         "additionalProperties": false,
         "description": "Perform a partial recursion of admissible DAGs",
         "properties": {
            "partial_recursion": {
               "$ref": "#/$defs/PartialRecursionConfig",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.search_model_graphs.recursion",
                     "ir_value_type": "short",
                     "storage_type": "PRESENCE_ENUM",
                     "stored_value": "PARTIAL_GRAPH_RECURSION"
                  }
               ]
            }
         },
         "required": [
            "partial_recursion"
         ],
         "title": "PartialRecursion",
         "type": "object"
      },
      "PartialRecursionConfig": {
         "additionalProperties": false,
         "description": "Perform a partial recursion of admissible DAGs",
         "properties": {
            "depth_limit": {
               "description": "Throttle the depth for a partial recursion of admissible DAGs",
               "title": "Depth Limit",
               "type": "integer",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.graph_depth_limit",
                     "ir_value_type": "unsigned short",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "required": [
            "depth_limit"
         ],
         "title": "PartialRecursionConfig",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "required": [
      "dag_recursion"
   ]
}

Fields:

• dag_recursion (dakota.spec.method.approximate_control_variate.ApproximateControlVariateSearchModelGraphsNoRecursion | dakota.spec.method.approximate_control_variate.KlRecursion | dakota.spec.method.approximate_control_variate.PartialRecursion | dakota.spec.method.approximate_control_variate.ApproximateControlVariateSearchModelGraphsFullRecursion)

• model_selection (Literal[True] | None)

field dag_recursion: ApproximateControlVariateSearchModelGraphsNoRecursion | KlRecursion | PartialRecursion | ApproximateControlVariateSearchModelGraphsFullRecursion [Required]

DAG Ensemble Generation Option

field model_selection: Literal[True] | None = None

Perform a recursion of admissible model subsets for a given model ensemble

pydantic model dakota.spec.method.approximate_control_variate.ApproximateControlVariateSearchModelGraphsFullRecursion

Perform a full recursion of all admissible DAGs for a given model ensemble

Show JSON schema

{
   "title": "ApproximateControlVariateSearchModelGraphsFullRecursion",
   "description": "Perform a full recursion of all admissible DAGs for a given model ensemble",
   "type": "object",
   "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"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• full_recursion (Literal[True])

field full_recursion: Literal[True] = True

Perform a full recursion of all admissible DAGs for a given model ensemble

pydantic model dakota.spec.method.approximate_control_variate.ApproximateControlVariateSearchModelGraphsNoRecursion

Do not recur over admissible DAGs for a given model ensemble

Show JSON schema

{
   "title": "ApproximateControlVariateSearchModelGraphsNoRecursion",
   "description": "Do not recur over admissible DAGs for a given model ensemble",
   "type": "object",
   "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"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• no_recursion (Literal[True])

field no_recursion: Literal[True] = True

Do not recur over admissible DAGs for a given model ensemble

pydantic model dakota.spec.method.approximate_control_variate.KlRecursion

Model graph enumeration that follows the ACV-KL partitioning scheme

Show JSON schema

{
   "title": "KlRecursion",
   "description": "Model graph enumeration that follows the ACV-KL partitioning scheme",
   "type": "object",
   "properties": {
      "kl_recursion": {
         "const": true,
         "default": true,
         "description": "Model graph enumeration that follows the ACV-KL partitioning scheme",
         "title": "Kl Recursion",
         "type": "boolean",
         "x-materialization": [
            {
               "ir_key": "method.nond.search_model_graphs.recursion",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "KL_GRAPH_RECURSION"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• kl_recursion (Literal[True])

field kl_recursion: Literal[True] = True

Model graph enumeration that follows the ACV-KL partitioning scheme

pydantic model dakota.spec.method.approximate_control_variate.PartialRecursion

Perform a partial recursion of admissible DAGs

Show JSON schema

{
   "title": "PartialRecursion",
   "description": "Perform a partial recursion of admissible DAGs",
   "type": "object",
   "properties": {
      "partial_recursion": {
         "$ref": "#/$defs/PartialRecursionConfig",
         "x-materialization": [
            {
               "ir_key": "method.nond.search_model_graphs.recursion",
               "ir_value_type": "short",
               "storage_type": "PRESENCE_ENUM",
               "stored_value": "PARTIAL_GRAPH_RECURSION"
            }
         ]
      }
   },
   "$defs": {
      "PartialRecursionConfig": {
         "additionalProperties": false,
         "description": "Perform a partial recursion of admissible DAGs",
         "properties": {
            "depth_limit": {
               "description": "Throttle the depth for a partial recursion of admissible DAGs",
               "title": "Depth Limit",
               "type": "integer",
               "x-materialization": [
                  {
                     "ir_key": "method.nond.graph_depth_limit",
                     "ir_value_type": "unsigned short",
                     "storage_type": "DIRECT_VALUE"
                  }
               ]
            }
         },
         "required": [
            "depth_limit"
         ],
         "title": "PartialRecursionConfig",
         "type": "object"
      }
   },
   "additionalProperties": false,
   "required": [
      "partial_recursion"
   ]
}

Fields:

• partial_recursion (dakota.spec.method.approximate_control_variate.PartialRecursionConfig)

field partial_recursion: PartialRecursionConfig [Required]

Perform a partial recursion of admissible DAGs

pydantic model dakota.spec.method.approximate_control_variate.PartialRecursionConfig

Perform a partial recursion of admissible DAGs

Show JSON schema

{
   "title": "PartialRecursionConfig",
   "description": "Perform a partial recursion of admissible DAGs",
   "type": "object",
   "properties": {
      "depth_limit": {
         "description": "Throttle the depth for a partial recursion of admissible DAGs",
         "title": "Depth Limit",
         "type": "integer",
         "x-materialization": [
            {
               "ir_key": "method.nond.graph_depth_limit",
               "ir_value_type": "unsigned short",
               "storage_type": "DIRECT_VALUE"
            }
         ]
      }
   },
   "additionalProperties": false,
   "required": [
      "depth_limit"
   ]
}

Fields:

• depth_limit (int)

field depth_limit: int [Required]

Throttle the depth for a partial recursion of admissible DAGs