Dakota Reference Manual  Version 6.15
Explore and Predict with Confidence
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auto_refinement


Experimental auto-refinement of surrogate model

Topics

This keyword is related to the topics:

Specification

Alias: none

Argument(s): 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.