samples
Number of samples for sampling-based methods
Specification
Alias: initial_samples
Arguments: INTEGER
Default: 0
Description
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).
Examples
method
sampling
sample_type lhs
samples = 20