Dakota Reference Manual: use_surrogate

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use_surrogate

Surrogate model usage mode for mesh adaptive search

## Specification

Alias: none

Argument(s): none

Default: optimize

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required (Choose One) | Surrogate Purpose (Group 1) | inform_search | Surrogate informs evaluation order in mesh adaptive search
optimize | Surrogate is used in lieu of true model for mesh adaptive search

## Description

The `use_surrogate` keyword is used to define how a surrogate model (if one is provided) is to be used by `mesh_adaptive_search`. There are two approaches available: `inform_search` uses the surrogate to sort list of trial points and subsequently the true function is evaluated on the most promising points first. Both true function and surrogate are used interchangeably within the method. `optimize` forces the use of a surrogate in lieu of the true model and thus the true function is never invoked except to construct the surrogate.

Known Issue: When using discrete variables, there have been sometimes significant differences in surrogate behavior observed across computing platforms in some cases. The cause has not yet been fully diagnosed and is currently under investigation. In addition, guidance on appropriate construction and use of surrogates with discrete variables is under development. In the meantime, users should therefore be aware that there is a risk of inaccurate results when using surrogates with discrete variables.

Default Behavior

By default, `mesh_adaptive_search` follows behaviour provided by `optimize` option.

## Examples

The following example shows the syntax used to set `use_surrogate`.

    method,
        mesh_adaptive_search
        model_pointer = 'SURROGATE'
        use_surrogate inform_search

    model,
        id_model = 'SURROGATE'
        surrogate global
        polynomial quadratic
        dace_method_pointer = 'SAMPLING'

    variables,
        continuous_design = 3
          initial_point   -1.0    1.5   2.0
          upper_bounds    10.0   10.0  10.0
          lower_bounds   -10.0  -10.0 -10.0
          descriptors      'x1'  'x2'  'x3'
        discrete_design_range = 2
          initial_point      2     2
          lower_bounds       1     1
          upper_bounds       4     9
          descriptors      'y1'   'y2'
           discrete_design_set
             real = 2
               elements_per_variable = 4 5
               elements = 1.2 2.3 3.4 4.5 1.2 3.3 4.4 5.5 7.7
               descriptors      'y3'   'y4'
             integer = 2
               elements_per_variable = 2 2
               elements = 4 7 8 9
               descriptors      'z1'   'z2'

    method,
        id_method = 'SAMPLING'
        model_pointer = 'TRUTH'
        sampling
          samples = 55

    model,
        id_model = 'TRUTH'
        single
          interface_pointer = 'TRUE_FN'

    interface,
        id_interface = 'TRUE_FN'
           direct
          analysis_driver = 'text_book'

    responses,
        objective_functions = 1
        no_gradients
        no_hessians