Dakota Reference Manual  Version 6.12
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Design and Analysis of Computer Experiments


This keyword is related to the topics:


Alias: none

Argument(s): none

Child Keywords:

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
(Choose One)
DACE type (Group 1) grid Grid Sampling

Uses purely random Monte Carlo sampling to sample variables

oas Orthogonal Array Sampling

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


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

See Also

These keywords may also be of interest: