Dakota Reference Manual: interface_selection

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interface_selection

Select the models that write evaluation data to HDF5

## Topics

This keyword is related to the topics:

- dakota_output

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required (Choose One) | Interface Evaluation Storage Selection (Group 1) | none | Write evaluation data for no interfaces to HDF5
simulation | Write evaluation data only for simulation interfaces to HDF5
all | Write evaluation data for all interfaces to HDF5

## Description

By default, when HDF5 output is enabled, Dakota writes evaluation data for all simulation interfaces (interfaces of type fork, system, direct, etc, but not approximation interfaces, which are constructed by models of type surrogate). This keyword group is used to override the default.

HDF5 output is an experimental feature, and the contents and organization of the output file is subject to change. The current organization and a brief explanation of HDF5 is provided in the Dakota HDF5 Output section of this manual.

The example below will be used to explain the effect of each keyword.

## Examples

    environment
      results_output
          hdf5
           # interface_selection
           #  simulation
           #  all
           #  none
          results_output_file 'my_results'  # The .h5 extension will be added

       method_pointer 'opt'

    method
      id_method 'opt'
        optpp_q_newton
      model_pointer 'surr'

    model
      id_model 'surr'
      surrogate global gaussian_process surfpack
      dace_method_pointer 'training'

    method
      id_method 'training'
      sampling
        seed 1234
        samples 20
      model_pointer 'truth_m'

    model
      id_model 'truth_m'
      simulation

    interface
      id_interface 'truth'
      direct
        analysis_drivers 'text_book'

    variables
      continuous_design 2
        descriptors 'x1' 'x2'
        lower_bounds -2.0 -2.0
        upper_bounds  2.0  2.0

    responses
      objective_functions 2
        descriptors 'f1' 'f2'
      analytic_gradients
      no_hessians