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:
low_discrepancy
keyword.Discussion of low-disrepancy methods.
Improvements by Category
Interfaces, Input/Output
Copying of
dakota.interfacing
objects was improved by adding__deepcopy__
implementationsNew examples:
Demonstrating use of Dakota’s direct
python
interface with a pre-built tensorflow model.Demonstrating use of
dakota.interfacing.dprepro
in a black-box interface. (For Windows and Linux/macOS)
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 thedakota.interfacing
module now propertly interprets the dvv list provided by Dakota’s directpython
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