Dakota Reference Manual: backfill

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

- Main Page
- Related Pages

All Pages
backfill

Ensures that the samples of discrete variables with finite support are unique

## Specification

Alias: none

Argument(s): none

## Description

Traditional LHS can generate replicate samples when applied to discrete variables. This keyword enforces uniqueness, which is determined only over the set of discrete variables with finite support. This allows one to generate LHS for a mixed set of continuous and discrete variables whilst still enforcing that the set of discrete LHS components of all the samples are unique.

Default Behavior

Uniqueness of samples over discrete variables is not enforced.

Usage Tips

Uniqueness can be useful when applying discrete LHS to simulations without noise.

## Examples

    method,
            sampling
              samples = 12
              seed = 123456
              sample_type lhs backfill

    variables,
            active all
            uniform_uncertain = 1
              lower_bounds =  0.
              upper_bounds =  1.
              descriptors  = 'continuous-uniform'

            discrete_uncertain_set
              integer = 1
               elements_per_variable = 4
                elements  1 3 5 7
                descriptors =    'design-set-int'
              real = 1
                initial_point = 0.50
                set_values  =  0.25 0.50 0.75 1.00
                descriptors =  'design-set-real'

    interface,
            direct analysis_driver = 'text_book'

    responses,
            response_functions = 3
            no_gradients
            no_hessians

## See Also

These keywords may also be of interest:

- lhs