Version 6.19 (2023/11/15; pre-release)

Highlight: New sampling-based method for 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 obtain esimates of main and total effects using a “pick and freeze” sampling strategy, which typically required a very large number of samples (hundreds or thousands per variable) and for those samples to be structured in a particular way. 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:

Improvements by Category

Interfaces, Input/Output

  • Copying of dakota.interfacing objects was improved by adding __deepcopy__ implementations

  • New examples:

Models

Optimization Methods

UQ Methods

  • Improved support for the MIT Uncertainty Quantification Library (MUQ); although MUQ is not enabled in our pre-built downloads, it is now buildable within Dakota by a wider variety of toolchains.

  • Low discrepancy sampling strategies (see highlight)

MLMF Sampling

Sensitivity Analysis

  • Binned method for sampling-based variance-based decomposition (see highlight)

  • New examples explaining use of correlation coefficients for global sensitivity analysis.

Miscellaneous Enhancements and Bugfixes

  • Enh: Documentation of Dakota’s regresion test system expanded.

  • Bug fix: The @python_interface decorator in the dakota.interfacing module now propertly interprets the dvv list provided by Dakota’s direct python interface.

  • Bug fix: RPATH handling on Linux-based platforms improved.

Deprecated and Changed

Compatibility

  • Support for building Dakota with C++17 has been greatly expanded and is expected to work for GCC, Intel, and Clang compilers. Support for Microsoft Visual Studio in progress.

Other Notes and Known Issues