model
Specifies how variables are mapped into a set of responses
Topics
block
Specification
Alias: None
Arguments: None
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|
Optional |
Give the model block an identifying name, in case of multiple model blocks |
||
Required (Choose One) |
Model Type |
A model with one of each block: variable, interface, and response |
|
An empirical model that is created from data or the results of a submodel |
|||
A model whose responses are computed through the use of a sub-iterator |
|||
Active (variable) subspace model |
|||
Basis adaptation model |
|||
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 |
Specify which variables block will be included with this model block |
||
Optional |
Specify which reponses block will be used by this model block |
||
Optional |
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:
single
(default)surrogate
nested
subspace
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.