Dakota Reference Manual
Version 6.10
Explore and Predict with Confidence

Performs an incremental Latin Hypercube Sampling (LHS) study
Alias: none
Argument(s): INTEGERLIST
Use of refinement_samples
replaces the deprecated sample_type
incremental_lhs
and sample_type
incremental_random
.
An incremental random sampling approach will successively add samples to an initial or existing random sampling study according to the sequence of refinement_samples
. Dakota reports statistics (mean, variance, percentiles, etc) separately for the initial samples
and for each refinement_samples
increment at the end of the study. For an LHS design, the number of refinement_samples
in each refinement level must result in twice the number of previous samples. For sample_type
random
, there is no constraint on the number of samples that can be added.
Often, this approach is used when you have an initial study with sample size N1 and you want to perform an additional N1 samples. The initial N1 samples may be contained in a Dakota restart file so only N1 (instead of 2 x N1) potentially expensive function evaluations will be performed.
This process can be extended by repeatedly increasing (for LHS: doubling) the refinement_samples:
method sampling seed = 1337 samples = 50 refinement_samples = 50 100 200 400 800
Usage Tips
The incremental approach is useful if it is uncertain how many simulations can be completed within available time.
See the examples below and the Usage and Restarting Dakota Studies pages.
Suppose an initial study is conducted using sample_type
lhs
with samples
= 50. A followon study uses a new input file where samples
= 50, and refinement_samples
= 50, resulting in 50 reused samples (from restart) and 50 new random samples. The 50 new samples will be combined with the 50 previous samples to generate a combined sample of size 100 for the analysis.
One way to ensure the restart file is saved is to specify a nondefault name, via a command line option:
dakota input LHS_50.in write_restart LHS_50.rst
which uses the input file:
# LHS_50.in environment tabular_data tabular_data_file = 'LHS_50.dat' method sampling seed = 1337 sample_type lhs samples = 50 model single variables uniform_uncertain = 2 descriptors = 'input1' 'input2' lower_bounds = 2.0 2.0 upper_bounds = 2.0 2.0 interface analysis_drivers 'text_book' fork responses response_functions = 1 no_gradients no_hessians
and the restart file is written to LHS_50.rst
.
Then an incremental LHS study can be run with:
dakota input LHS_100.in read_restart LHS_50.rst write_restart LHS_100.rst
where LHS_100.in
is shown below, and LHS_50.rst
is the restart file containing the results of the previous LHS study. In the example input files for the initial and incremental studies, the values for seed
match. This ensures that the initial 50 samples generated in both runs are the same.
# LHS_100.in environment tabular_data tabular_data_file = 'LHS_100.dat' method sampling seed = 1337 sample_type lhs samples = 50 refinement_samples = 50 model single variables uniform_uncertain = 2 descriptors = 'input1' 'input2' lower_bounds = 2.0 2.0 upper_bounds = 2.0 2.0 interface analysis_drivers 'text_book' fork responses response_functions = 1 no_gradients no_hessians
The user will get 50 new LHS samples which maintain both the correlation and stratification of the original LHS sample. The new samples will be combined with the original samples to generate a combined sample of size 100.