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 topic-variable_support for information on supported variable types.