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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Design of Computer Experiments - Centroidal Voronoi Tessellation
This keyword is related to the topics:
Alias: none
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Optional | latinize | Adjust samples to improve the discrepancy of the marginal distributions | ||
| 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 | fixed_seed | Reuses the same seed value for multiple random sampling sets | ||
| Optional | trial_type | Specify how the trial samples are generated | ||
| Optional | num_trials | The number of secondary sample points generated to adjust the location of the primary sample points | ||
| Optional | samples | Number of samples for sampling-based methods | ||
| Optional | seed | Seed of the random number generator | ||
| Optional | model_pointer | Identifier for model block to be used by a method | ||
The FSU Centroidal Voronoi Tessellation method (fsu_cvt) produces a set of sample points that are (approximately) a Centroidal Voronoi Tessellation. The primary feature of such a set of points is that they have good volumetric spacing; the points tend to arrange themselves in a pattern of cells that are roughly the same shape.
To produce this set of points, an almost arbitrary set of initial points is chosen, and then an internal set of iterations is carried out. These iterations repeatedly replace the current set of sample points by an estimate of the centroids of the corresponding Voronoi subregions. [17].
The user may generally ignore the details of this internal iteration. If control is desired, however, there are a few variables with which the user can influence the iteration. The user may specify:
max_iterations, the number of iterations carried out (this is a method independent control, see max_iterations num_trials, the number of secondary sample points generated to adjust the location of the primary sample points trial_type, which controls how these secondary sample points are generatedThis method generates sets of uniform random variables on the interval [0,1]. If the user specifies lower and upper bounds for a variable, the [0,1] samples are mapped to the [lower, upper] interval.
This method is designed to generate samples with the goal of low discrepancy. Discrepancy refers to the nonuniformity of the sample points within the hypercube.
Discrepancy is defined as the difference between the actual number and the expected number of points one would expect in a particular set B (such as a hyper-rectangle within the unit hypercube), maximized over all such sets. Low discrepancy sequences tend to cover the unit hypercube reasonably uniformly.
Centroidal Voronoi Tessellation does very well volumetrically: it spaces the points fairly equally throughout the space, so that the points cover the region and are isotropically distributed with no directional bias in the point placement. There are various measures of volumetric uniformity which take into account the distances between pairs of points, regularity measures, etc. Note that Centroidal Voronoi Tessellation does not produce low-discrepancy sequences in lower dimensions. The lower-dimension (such as 1-D) projections of Centroidal Voronoi Tessellation can have high discrepancy.
These keywords may also be of interest:
1.8.4