Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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Experimental auto-refinement of surrogate model


This keyword is related to the topics:


Alias: none

Argument(s): none

Default: no refinement

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional max_iterations

Stopping criterion based on number of iterations

Optional max_function_evaluations Stopping criteria based on number of function evaluations
Optional convergence_tolerance

Cross-validation threshold for surrogate convergence

Optional cross_validation_metric

Choice of error metric to satisfy


(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.

Default Behavior By default, the surrogate will not be iteratively refined.