 |
Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
|
|
Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
|
Global reliability methods
This keyword is related to the topics:
Alias: nond_global_reliability
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Required (Choose One) | Group 1 | x_gaussian_process | Create GP surrogate in x-space | |
| u_gaussian_process | Create GP surrogate in u-space | |||
| Optional (Choose One) | Group 2 | surfpack | Use the Surfpack version of Gaussian Process surrogates | |
| dakota | Select the built in Gaussian Process surrogate | |||
| Optional | import_points_file | File containing variable values and corresponding responses | ||
| Optional | export_points_file | Output file for evaluations of a surrogate model | ||
| Optional | use_derivatives | Use derivative data to construct surrogate models | ||
| Optional | seed | Seed of the random number generator | ||
| Optional | rng | Selection of a random number generator | ||
| Optional | response_levels | Values at which to estimate desired statistics for each response | ||
| Optional | distribution | Selection of cumulative or complementary cumulative functions | ||
| Optional | probability_levels | Specify probability levels at which to estimate the corresponding response value | ||
| Optional | gen_reliability_levels | Specify generalized relability levels at which to estimate the corresponding response value | ||
| Optional | model_pointer | Identifier for model block to be used by a method | ||
These methods do not support forward/inverse mappings involving reliability_levels, since they never form a reliability index based on distance in u-space. Rather they use a Gaussian process model to form an approximation to the limit state (based either in x-space via the x_gaussian_process specification or in u-space via the u_gaussian_process specification), followed by probability estimation based on multimodal adaptive importance sampling (see [10]) and [11]). These probability estimates may then be transformed into generalized reliability levels if desired. At this time, inverse reliability analysis (mapping probability or generalized reliability levels into response levels) is not implemented.
The Gaussian process model approximation to the limit state is formed over the aleatory uncertain variables by default, but may be extended to also capture the effect of design, epistemic uncertain, and state variables. If this is desired, one must use the appropriate controls to specify the active variables in the variables specification block. Refer to variable_support for additional information on supported variable types.
These keywords may also be of interest:
1.8.4 
