global_interval_est

Interval analysis using global optimization methods

Topics

uncertainty_quantification, epistemic_uncertainty_quantification_methods, interval_estimation

Specification

  • Alias: nond_global_interval_est

  • Arguments: None

Child Keywords:

Required/Optional

Description of Group

Dakota Keyword

Dakota Keyword Description

Optional

samples

Number of samples for sampling-based methods

Optional

seed

Seed of the random number generator

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

max_function_evaluations

Number of function evaluations allowed for optimizers

Optional (Choose One)

Solution Approach

sbgo

Use the surrogate based optimization method

ego

Use the Efficient Global Optimization method

ea

Use an evolutionary algorithm

lhs

Uses Latin Hypercube Sampling (LHS) to sample variables

Optional

rng

Selection of a random number generator

Optional

model_pointer

Identifier for model block to be used by a method

Description

In the global approach to interval estimation, one uses either a global optimization method or a sampling method to assess the bounds of the responses.

global_interval_est allows the user to specify several approaches to calculate interval bounds on the output responses.

  • lhs - note: this takes the minimum and maximum of the samples as the bounds

  • ego

  • sbo

  • ea

Additional Resources

Refer to Variable Support for information on supported variable types.