initial_samples
Initial number of samples for sampling-based methods
Specification
Alias: None
Arguments: INTEGER
Default: model-dependent
Description
The initial_samples
keyword is used to define the number of initial
samples (i.e., randomly chosen sets of variable values) at which to
execute a model. The initial samples may later be augmented in an
iterative process.
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 random
initial_samples = 20
refinement_samples = 5