auto_refinement

Experimental auto-refinement of surrogate model

Topics

surrogate_models

Specification

  • Alias: None

  • Arguments: None

  • Default: no refinement

Child Keywords:

Required/Optional

Description of Group

Dakota Keyword

Dakota Keyword Description

Optional

max_iterations

Number of iterations allowed for optimizers and adaptive UQ methods

Optional

max_function_evaluations

Number of function evaluations allowed for optimizers

Optional

convergence_tolerance

Cross-validation threshold for surrogate convergence

Optional

soft_convergence_limit

Maximum number of iterations without improvement in cross-validation

Optional

cross_validation_metric

Choice of error metric to satisfy

Description

(Experimental option) Automatically refine the surrogate model until desired cross-validation quality is achieved. Refinement is accomplished by iteratively adding more data to the training set until the cross-validation convergence_tolerance is achieved, or max_function_evaluations or max_iterations is exceeded.

The amount of new training data that is incorporated each iteration is specified in the DACE method that is referred to by the model’s dace_method_pointer. See refinement_samples for more information.