dace

Design and Analysis of Computer Experiments

Topics

package_ddace, design_and_analysis_of_computer_experiments

Specification

• Alias: None

• Arguments: None

Child Keywords:

Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
Required (Choose One)	DACE type	grid	Grid Sampling
		random	Uses purely random Monte Carlo sampling to sample variables
		oas	Orthogonal Array Sampling
		lhs	Uses Latin Hypercube Sampling (LHS) to sample variables
		oa_lhs	Orthogonal Array Latin Hypercube Sampling
		box_behnken	Box-Behnken Design
		central_composite	Central Composite Design
Optional		samples	Number of samples for sampling-based methods
Optional		seed	Seed of the random number generator
Optional		fixed_seed	Reuses the same seed value for multiple random sampling sets
Optional		main_effects	ANOVA
Optional		quality_metrics	Calculate metrics to assess the quality of quasi-Monte Carlo samples
Optional		variance_based_decomp	Activates global sensitivity analysis based on decomposition of response variance into contributions from variables
Optional		symbols	Number of replications in the sample set
Optional		model_pointer	Identifier for model block to be used by a method

Description

The Distributed Design and Analysis of Computer Experiments (DDACE) library provides the following DACE techniques:

1. grid sampling ( grid)

2. pure random sampling ( random)

3. orthogonal array sampling ( oas)

4. latin hypercube sampling ( lhs)

5. orthogonal array latin hypercube sampling ( oa_lhs)

6. Box-Behnken ( box_behnken)

7. central composite design ( central_composite)

These methods all generate point sets that may be used to drive a set of computer experiments. Note that all of the DACE methods generated randomized designs, except for Box-Behnken and Central composite which are classical designs. That is, the grid sampling will generate a randomized grid, not what one typically thinks of as a grid of uniformly spaced points over a rectangular grid. Similar, the orthogonal array is a randomized version of an orthogonal array: it does not generate discrete, fixed levels.

In addition to the selection of the method, there are keywords that affect the method outputs:

1. main_effects

2. quality_metrics

3. variance_based_decomp

And keywords that affect the sampling:

1. fixed_seed

2. symbols

3. samples

4. seed