Dakota Reference Manual  Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Specifies how variables are mapped into a set of responses


This keyword is related to the topics:


Alias: none

Argument(s): none

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
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
(Choose One)
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


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
  2. nested
  3. surrogate

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.


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.

  id_model = 'M1'
  variables_pointer = 'V1'
  responses_pointer = 'R1'
      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

    id_model = 'M1'
    variables_pointer = 'V1'
    responses_pointer = 'R1'
     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 also supplies model independent controls for model identifier, variables pointer, and responses pointer and supplies model dependent controls for specifying details of the nested mapping.