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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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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
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 |
(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.