Dakota Reference Manual: auto_refinement

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auto_refinement

Experimental auto-refinement of surrogate model

## Topics

This keyword is related to the topics:

- surrogate_models

## 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.