Dakota Reference Manual  Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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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
(Choose One)
Group 1 x_gaussian_process Create GP surrogate in x-space
u_gaussian_process Create GP surrogate in u-space
(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.

See Also

These keywords may also be of interest: