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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Generic response type
Alias: num_response_functions
Argument(s): INTEGER
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Optional | scalar_responses | Number of scalar response functions | ||
Optional | field_responses | Number of field responses functions |
A generic response data set is specified using response_functions
. Each of these functions is simply a response quantity of interest with no special interpretation taken by the method in use.
Whereas objective, constraint, and residual functions have special meanings for optimization and least squares algorithms, the generic response function data set need not have a specific interpretation and the user is free to define whatever functional form is convenient.
This type of data set is used by uncertainty quantification methods, in which the effect of parameter uncertainty on response functions is quantified, and can also be used in parameter study and design of experiments methods (although these methods are not restricted to this data set), in which the effect of parameter variations on response functions is evaluated.
These keywords may also be of interest: