![]() |
Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
|
Surrogate model usage mode for mesh adaptive search
Alias: none
Argument(s): none
Default: optimize
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Required (Choose One) | Surrogate Purpose (Group 1) | inform_search | Surrogate informs evaluation order in mesh adaptive search | |
optimize | Surrogate is used in lieu of true model for mesh adaptive search |
The use_surrogate
keyword is used to define how a surrogate model (if one is provided) is to be used by mesh_adaptive_search
. There are two approaches available: inform_search
uses the surrogate to sort list of trial points and subsequently the true function is evaluated on the most promising points first. Both true function and surrogate are used interchangeably within the method. optimize
forces the use of a surrogate in lieu of the true model and thus the true function is never invoked except to construct the surrogate.
Known Issue: When using discrete variables, there have been sometimes significant differences in surrogate behavior observed across computing platforms in some cases. The cause has not yet been fully diagnosed and is currently under investigation. In addition, guidance on appropriate construction and use of surrogates with discrete variables is under development. In the meantime, users should therefore be aware that there is a risk of inaccurate results when using surrogates with discrete variables.
Default Behavior
By default, mesh_adaptive_search
follows behaviour provided by optimize
option.
The following example shows the syntax used to set use_surrogate
.
method, mesh_adaptive_search model_pointer = 'SURROGATE' use_surrogate inform_search model, id_model = 'SURROGATE' surrogate global polynomial quadratic dace_method_pointer = 'SAMPLING' variables, continuous_design = 3 initial_point -1.0 1.5 2.0 upper_bounds 10.0 10.0 10.0 lower_bounds -10.0 -10.0 -10.0 descriptors 'x1' 'x2' 'x3' discrete_design_range = 2 initial_point 2 2 lower_bounds 1 1 upper_bounds 4 9 descriptors 'y1' 'y2' discrete_design_set real = 2 elements_per_variable = 4 5 elements = 1.2 2.3 3.4 4.5 1.2 3.3 4.4 5.5 7.7 descriptors 'y3' 'y4' integer = 2 elements_per_variable = 2 2 elements = 4 7 8 9 descriptors 'z1' 'z2' method, id_method = 'SAMPLING' model_pointer = 'TRUTH' sampling samples = 55 model, id_model = 'TRUTH' single interface_pointer = 'TRUE_FN' interface, id_interface = 'TRUE_FN' direct analysis_driver = 'text_book' responses, objective_functions = 1 no_gradients no_hessians