Dakota Reference Manual  Version 6.12
Explore and Predict with Confidence
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Global reliability methods


This keyword is related to the topics:


Alias: nond_global_reliability

Argument(s): none

Child Keywords:

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional initial_samples

Initial number of samples for sampling-based methods

(Choose One)
Approximation (Group 1) x_gaussian_process Create GP surrogate in x-space
u_gaussian_process Create GP surrogate in u-space
(Choose One)
GP Implementation (Group 2) surfpack Use the Surfpack version of Gaussian Process surrogates
dakota Select the built in Gaussian Process surrogate
Optional import_build_points_file

File containing points you wish to use to build a surrogate

Optional export_approx_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 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 distribution

Selection of cumulative or complementary cumulative functions

Optional max_iterations

Number of iterations allowed for optimizers and adaptive UQ methods

Optional convergence_tolerance

Stopping criterion based on objective function or statistics convergence

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: