multilevel_blue
- pydantic model dakota.spec.method.multilevel_blue.MultilevelBlueSelection
Generated model for MultilevelBlueSelection
Show JSON schema
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"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" }, "PilotSamples": { "additionalProperties": false, "description": "Initial set of samples for groups in the multilevel BLUE sampling method", "properties": { "counts": { "description": "Initial set of samples for groups in the multilevel BLUE sampling method", "items": { "type": "integer" }, "title": "Counts", "type": "array", "x-materialization": [ { "ir_key": "method.nond.pilot_samples", "ir_value_type": "SizetArray", "storage_type": "DIRECT_VALUE" } ] }, "independent": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Independent pilot sampling for groups in multilevel BLUE", "title": "Independent", "x-materialization": [ { "ir_key": "method.nond.pilot_samples.mode", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "INDEPENDENT_PILOT" } ] } }, "required": [ "counts" ], "title": "PilotSamples", "type": "object", "x-model-validations": [ { "validationContext": "mlmfgrouppilotsamplespilotsamples", "validationErrorMessage": "For mlmfgrouppilotsamplespilotsamples, all elements of counts must be >= 0.", "validationFields": [ "counts" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }, "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" }, "RcondTol": { "additionalProperties": false, "description": "Throttle multilevel BLUE to only solve for allocations using groups with prescribed conditioning in their group covariances", "properties": { "rcond_tolerance": { "description": "Throttle multilevel BLUE to only solve for allocations using groups with prescribed conditioning in their group covariances", "minimum": 0, "title": "Rcond Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.nond.rcond_tol_throttle", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "rcond_tolerance" ], "title": "RcondTol", "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": [ "multilevel_blue" ] }
- field multilevel_blue: MultilevelBlueConfig [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_blue.MultilevelBlueConfig
The multilevel best linear unbiased estimator (ML BLUE) sampling method for UQ
Show JSON schema
{ "title": "MultilevelBlueConfig", "description": "The multilevel best linear unbiased estimator (ML BLUE) 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": "Number of iterations allowed for optimizers and adaptive UQ methods", "minimum": 0, "title": "Max Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "convergence_tolerance": { "anyOf": [ { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext3ConvergenceTol" }, { "type": "null" } ], "argument": "value", "default": null, "description": "Stopping criterion based on relative error reduction" }, "sample_type": { "anyOf": [ { "$ref": "#/$defs/MethodSampleTypeLhsMcLhs" }, { "$ref": "#/$defs/MethodSampleTypeLhsMcRandom" }, { "type": "null" } ], "default": null, "description": "Selection of sampling strategy", "title": "Sample Type", "x-union-pattern": 2 }, "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" } ] }, "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 }, "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": [ { "$ref": "#/$defs/PilotSamples" }, { "type": "null" } ], "argument": "counts", "default": null, "description": "Initial set of samples for groups in the multilevel BLUE sampling method", "x-aliases": [ "initial_samples" ] }, "group_throttle": { "anyOf": [ { "$ref": "#/$defs/MfmcGroups" }, { "$ref": "#/$defs/CommonGroups" }, { "$ref": "#/$defs/GroupSize" }, { "$ref": "#/$defs/BestConditioned" }, { "$ref": "#/$defs/RcondTol" }, { "type": "null" } ], "default": null, "description": "Reduce the number of groups in multilevel BLUE using a throttle", "title": "Group Throttle", "x-union-pattern": 2 }, "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" } ] }, "export_sample_sequence": { "anyOf": [ { "$ref": "#/$defs/MultilevelBlueExportSampleSequence" }, { "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" } ] } }, "$defs": { "BestConditioned": { "additionalProperties": false, "description": "Throttle multilevel BLUE to only solve for allocations using the groups with the best conditioning in their group covariances", "properties": { "best_conditioned": { "description": "Throttle multilevel BLUE to only solve for allocations using the groups with the best conditioning in their group covariances", "exclusiveMinimum": 0, "title": "Best Conditioned", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.rcond_best_throttle", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "best_conditioned" ], "title": "BestConditioned", "type": "object" }, "CommonGroups": { "additionalProperties": false, "description": "Throttle multilevel BLUE to only search over common groups", "properties": { "common_groups": { "const": true, "default": true, "description": "Throttle multilevel BLUE to only search over common groups", "title": "Common Groups", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.group_throttle_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMMON_ESTIMATOR_GROUPS" } ] } }, "title": "CommonGroups", "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" }, "GroupSize": { "additionalProperties": false, "description": "Throttle multilevel BLUE to only search over groups of a maximum size", "properties": { "group_size": { "description": "Throttle multilevel BLUE to only search over groups of a maximum size", "exclusiveMinimum": 0, "title": "Group Size", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.group_size_throttle", "ir_value_type": "unsigned short", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "group_size" ], "title": "GroupSize", "type": "object" }, "MethodConvergenceTolWithTypeContext3Absolute": { "additionalProperties": false, "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods", "properties": { "absolute": { "const": true, "default": true, "description": "Use absolute statistical metrics for assessing convergence in adaptive UQ methods", "title": "Absolute", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.convergence_tolerance_type", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ABSOLUTE_CONVERGENCE_TOLERANCE" } ] } }, "title": "MethodConvergenceTolWithTypeContext3Absolute", "type": "object" }, "MethodConvergenceTolWithTypeContext3ConvergenceTol": { "additionalProperties": false, "description": "Stopping criterion based on relative error reduction", "properties": { "value": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on relative error reduction", "title": "Value", "type": "number", "x-materialization": [ { "ir_key": "method.convergence_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" }, { "ir_key": "method.jega.percent_change", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "convergence_tolerance_type": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext3Relative" }, { "$ref": "#/$defs/MethodConvergenceTolWithTypeContext3Absolute" }, { "type": "null" } ], "default": null, "description": "Convergence tolerance type", "title": "Convergence Tolerance Type", "x-union-pattern": 2 } }, "title": "MethodConvergenceTolWithTypeContext3ConvergenceTol", "type": "object" }, "MethodConvergenceTolWithTypeContext3Relative": { "additionalProperties": false, "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark", "properties": { "relative": { "const": true, "default": true, "description": "Assess convergence in adaptive UQ methods using statistical metrics that are relative to a benchmark", "title": "Relative", "type": 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"description": "Level 1 of 5 - minimum", "title": "Silent", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SILENT_OUTPUT" } ] } }, "title": "Silent", "type": "object" }, "Verbose": { "additionalProperties": false, "description": "Level 4 of 5 - more than normal", "properties": { "verbose": { "const": true, "default": true, "description": "Level 4 of 5 - more than normal", "title": "Verbose", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VERBOSE_OUTPUT" } ] } }, "title": "Verbose", "type": "object" } }, "additionalProperties": false, "x-model-validations": [ { "validationContext": "methodseedsequencemixin", "validationErrorMessage": "For methodseedsequencemixin, all elements of seed_sequence must be >= 0.", "validationFields": [ "seed_sequence" ], "validationLiterals": [], "validationRuleName": "check_nonnegative_list" } ] }
- field convergence_tolerance: MethodConvergenceTolWithTypeContext3ConvergenceTol | None = None
Stopping criterion based on relative error reduction
- field export_sample_sequence: MultilevelBlueExportSampleSequence | 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 group_throttle: MfmcGroups | CommonGroups | GroupSize | BestConditioned | RcondTol | None = None
Reduce the number of groups in multilevel BLUE using a throttle
- 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: PilotSamples | None = None
Initial set of samples for groups in the multilevel BLUE sampling method
- 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 solver_metric: MethodMlmfSolverMetricAverageEstimatorVariance | MethodMlmfSolverMetricNormEstimatorVariance | MethodMlmfSolverMetricMaxEstimatorVariance | None = None
Metric employed during numerical solutions in sampling-based multifidelity UQ methods.
Generated Pydantic models for method.multilevel_blue
- pydantic model dakota.spec.method.multilevel_blue.MultilevelBlueExportSampleSequence
Enable export of multilevel/multifidelity sample sequences to individual files
Show JSON schema
{ "title": "MultilevelBlueExportSampleSequence", "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 }
- field format: MethodExportSamplesFormatCustomAnnotated | MethodExportSamplesFormatAnnotated | MethodExportSamplesFormatFreeform [Optional]
Tabular Format
