Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Estimate order of convergence of a response as model fidelity increases
Alias: none
Argument(s): none
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Required (Choose One)  Group 1  estimate_order  Compute the best estimate of the convergence order from three points  
converge_order  Refine until the estimated covergence order converges  
converge_qoi  Refine until the response converges  
Optional  refinement_rate  Rate at which the state variables are refined  
Optional  convergence_tolerance  Stopping criterion based on convergence of the objective function or statistics  
Optional  max_iterations  Stopping criterion based on number of iterations  
Optional  model_pointer  Identifier for model block to be used by a method 
Solution verification procedures estimate the order of convergence of the simulation response data during the course of a refinement study. This branch of methods is new and currently only contains one algorithm: Richardson extrapolation.
Refinement of the model
The model fidelity must be parameterized by one or more continuous state variable(s).
The refinement path is determined from the initial_state
of the continuous_state
variables specification in combination with the refinement_rate
, where each of the state variables is treated as an independent refinement factor and each of the initial state values is repeatedly divided by the refinement rate value to define new discretization states.
Results
Three algorithm options are currently provided:
estimate_order
converge_order
converge_qoi
Stopping Criteria
The method employs the max_iterations
and convergence_tolerance
method independent controls as stopping criteria.
In each of these cases, convergence order for a response quantity of interest (QoI) is estimated from
where is the uniform refinement rate specified by refinement_rate
.