Dakota Reference Manual
Version 6.4
LargeScale 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  

Optional  initial_samples  Initial number of samples for samplingbased methods  
Required (Choose One)  Group 1  x_gaussian_process  Create GP surrogate in xspace  
u_gaussian_process  Create GP surrogate in uspace  
Optional (Choose One)  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  max_iterations  Stopping criterion based on number of iterations  
Optional  convergence_tolerance  Stopping criterion based on convergence of the objective function or statistics  
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 uspace. Rather they use a Gaussian process model to form an approximation to the limit state (based either in xspace via the x_gaussian_process
specification or in uspace 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: