Announcing Dakota Version 6.19

Dakota 6.19 is officially available for download.

Highlight: New sampling-based method for Sobol’ main effects

Based on [LM16], Dakota can now obtain estimates of first order Sobol’ indices (main effects) from sampling studies. Previous versions of Dakota could estimate main and total effects using a “pick and freeze” sampling strategy [STCR04], which typically required a very large number of samples (hundreds or thousands per variable) that had to be carefully structured. While the new method produces only main effects, the requirement on sample design has been lifted, and typically far fewer samples are needed for convergence.

Enabling / Accessing:

The variance_based_decomp keyword now has suboptions. The vbd_sampling_method pick_and_freeze option is the default, and causes Dakota to use the method that has long been available to compute main and total effects. The vbd_sampling_method binned option causes the new method to be used.

Documentation:

Keyword reference for the binned VBD method.

Highlight: Low-discrepancy (quasi-Monte Carlo) sampling

Two new strategies for choosing low-discrepancy points in sampling studies are available in this release. These include lattice rules and digital nets. The well-known Sobol sequence is an example of a digital net. Just as in Latin hypercube sampling, these strategies choose points that cover the parameter space more uniformly than ordinary Monte Carlo, leading to faster convergence of UQ results.

Enabling / Accessing:

In a sampling study, choose sample_type low_discrepancy.

Documentation:

• low_discrepancy keyword.
• Discussion of low-disrepancy methods.

Highlight: Model selection in multifidelity sampling methods

MFMC, ACV, and generalized ACV now support selection of the most performant subset of model approximations, as determined through enumeration by the estimator accuracy versus equivalent cost trade-off.

Enabling / Accessing:

In a multifidelity_sampling or approximate_control_variate study, choose search_model_graphs model_selection.

Documentation:

• model_selection keyword.

For complete release notes, visit https://snl-dakota.github.io/docs/6.19.0/users/misc/releasenotes/619.html