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 |
Number of iterations allowed for optimizers and adaptive UQ methods |
||
Optional |
Number of function evaluations allowed for optimizers |
||
Optional |
Cross-validation threshold for surrogate convergence |
||
Optional |
Maximum number of iterations without improvement in cross-validation |
||
Optional |
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.