Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Number of samples for samplingbased methods
Alias: none
Argument(s): INTEGER
The samples
keyword is used to define the number of samples (i.e., randomly chosen sets of variable values) at which to execute a model.
Default Behavior
By default, Dakota will use the minimum number of samples required by the chosen method.
Usage Tips
To obtain linear sensitivities or to construct a linear response surface, at least dim+1 samples should be used, where "dim" is the number of variables. For sensitivities to quadratic terms or quadratic response surfaces, at least (dim+1)(dim+2)/2 samples are needed. For uncertainty quantification, we recommend at least 10*dim samples. For variance_based_decomp
, we recommend hundreds to thousands of samples. Note that for variance_based_decomp
, the number of simulations performed will be N*(dim+2).
method sampling sample_type lhs samples = 20