Dakota 6.21

[jadamstephens]

Dakota 6.21 is officially available for download.

Highlight: Numerical Robustness for ML Blue

The multilevel best linear unbiased estimator (ML BLUE) now uses truncated SVD for all matrix solutions (previously handled by Cholesky factorization with equilibration), enabling more robust solution recovery for resouce allocations and final moment estimates. This can mitigate ill-conditioned for problems with larger group counts, for groups containing some level of model redundancy, and for larger budgets with more aggressive sample profiles.

Enabling / Accessing: New SVD solver strategies are built-in with truncation tolerances tuned from numerical studies.

Documentation: Refer to the reference documentation starting from multilevel_blue for additional information on ML BLUE and its options. Benchmark examples with three models (tunable problem), five models (steady state diffusion), and 8 models (transient diffusion) are available under dakota/test in the release distribution.

Highlight: New options for using MCMC algorithms from the MUQ library

This release exposes the DILI (Dimension-Independent Likelihood-Informed subspace) MCMC sampler from the MIT UQ Library (MUQ).

Enabling / Accessing:

Add the dili keyword to the bayes_calibration muq method keyword group to select this submethod. MUQ is available in Linux and macOS downloads, but not yet in Windows. It must be enabled when building from source by setting the CMake variable DAKOTA_HAVE_MUQ=ON.

Documentation:

Consult Dakota’s keyword documentation for the dili method or the MUQ documentation.

Highlight: JSON parameters and results files

This release adds support for parameters and results files in JSON format, which is intended to simplify driver development.

Enabling / Accessing:

Two new keyword groups were added to the interface portion of the input file, parameters_format and results_format. The json keyword within these groups is used to select JSON format files.

In addition, the aprepro keyword was moved to the parameters_format group, and the labeled keyword is now located in the results_format group.

Documentation:

The User’s Manual describes the schemas for JSON parameters and results files.

For complete release notes, visit https://snl-dakota.github.io/docs/6.21.0/users/misc/releasenotes/621.html

What is Dakota?

The Dakota toolkit provides a flexible, extensible interface between analysis codes and iterative systems analysis methods. Dakota contains algorithms for:

• optimization with gradient and nongradient-based methods;
• uncertainty quantification with sampling, reliability, stochastic expansion, and epistemic methods;
• parameter estimation with nonlinear least squares and Bayesian methods; and
• sensitivity/variance analysis with design of experiments and parameter study methods.

These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. Learn more at https://dakota.sandia.gov/about-dakota/