Dakota Reference Manual: response_scaling

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

- Main Page
- Related Pages

All Pages
response_scaling

Perform bounds-scaling on response values prior to surrogate emulation

## Specification

Alias: none

Argument(s): none

Default: use original data

## Description

Particularly in multifidelity methods based on regression solutions at each level of a model hierarchy, scaling can be an issue since the magnitude of discrepancy data may decay rapidly and regression solvers may employ absolute tolerances in places.

By activating `response_scaling`, the set of response data used for each recovery is scaled to [0,1] based on the minimum value and range of the data set. This prevents loss of accuracy due to imbalances in scale across model levels.

## See Also

These keywords may also be of interest:

- multifidelity_polynomial_chaos
- multifidelity_function_train