Dakota Reference Manual: model

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model

Specifies how variables are mapped into a set of responses

## Topics

This keyword is related to the topics:

- block

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | id_model | Give the model block an identifying name, in case of multiple model blocks
| Required (Choose One) | Model Type (Group 1) | single | A model with one of each block: variable, interface, and response
surrogate | An empirical model that is created from data or the results of a submodel
nested | A model whose responses are computed through the use of a sub-iterator
active_subspace | Active (variable) subspace model
adapted_basis | Basis adaptation model
random_field | Experimental capability to generate a random field representation. from data, from simulation runs, or from a covariance matrix. The representation may then be sampled for use as a random field input to another simulation. THIS IS AN EXPERIMENTAL CAPABILITY.
| Optional | variables_pointer | Specify which variables block will be included with this model block
| Optional | responses_pointer | Specify which reponses block will be used by this model block
| Optional | hierarchical_tagging | Enables hierarchical evaluation tagging

## Description

A model is comprised of a mapping from variables, through an interface, to responses.

Model Group 1 The type of model can be:

1. `single` (default)
2. `surrogate`
3. `nested`
4. `subspace`
5. `random_field`

The input file must specify one of these types. If the type is not specified, Dakota will assume a single model.

Block Pointers and ID

Each of these model types supports `variables_pointer` and `responses_pointer` strings for identifying the variables and responses specifications used in constructing the model by cross-referencing with `id_variables` and `id_responses` strings from particular variables and responses keyword specifications.

These pointers are valid for each model type since each model contains a set of variables that is mapped into a set of responses – only the specifics of the mapping differ.

Additional pointers are used for each model type for constructing the components of the variable to response mapping. As an environment specification identifies a top-level method and a method specification identifies a model, a model specification identifies variables, responses, and (for some types) interface specifications. This top-down flow specifies all of the object interrelationships.

## Examples

The first example shows a minimal specification for a `single` model, which is the default model when no models are explicitly specified by the user.

    model
      single

The next example displays a surrogate model specification which selects a quadratic polynomial from among the global approximation methods. It uses a pointer to a design of experiments method for generating the data needed for building the global approximation, reuses any old data available for the current approximation region, and employs the first-order multiplicative approach to correcting the approximation each time correction is requested.

    model,
      id_model = 'M1'
      variables_pointer = 'V1'
      responses_pointer = 'R1'
      surrogate
        global
          polynomial quadratic
          dace_method_pointer = 'DACE'
          reuse_samples region
          correction multiplicative first_order

This example demonstrates the use of identifiers and pointers. It provides the optional model independent specifications for model identifier, variables pointer, and responses pointer as well as model dependent specifications for global surrogates (see global).

Finally, an advanced nested model example would be

    model
      id_model = 'M1'
      variables_pointer = 'V1'
      responses_pointer = 'R1'
      nested
        optional_interface_pointer = 'OI1'
          optional_interface_responses_pointer = 'OIR1'
         sub_method_pointer = 'SM1'
           primary_variable_mapping  = '' '' 'X'   'Y'
           secondary_variable_mapping = '' '' 'mean' 'mean'
           primary_response_mapping  = 1. 0. 0. 0. 0. 0. 0. 0. 0.
           secondary_response_mapping = 0. 0. 0. 1. 3. 0. 0. 0. 0.
                                        0. 0. 0. 0. 0. 0. 1. 3. 0.

This example illustrates controls for model identifier, variables pointer, and responses pointer and for specifying details of the nested mapping.