Version 6.22 (2025/05/15)

Improvements by Category

Interfaces, Input/Output

Optimization Methods

UQ Methods

  • Multifidelity sampling methods now summarize the final variance metrics per quantity of interest (QoI) and report 95% confidence intervals for each of the QoI means.

  • Multifidelity sampling methods now admit different reductions for forming a scalar optimization metric in multiple QoI cases: max, average, and p-norm can now be used during sample allocation solves. Refer to optimization metric.

  • ML BLUE now employs analytic gradients of the estimator variance to improve solution robustness.

  • All multifidelity sampling methods (previously only ML BLUE, but now including all ACV and generalized ACV solves) now employ the same truncated SVD mitigations for ill-conditioned matrix solves. Previous Cholesky factorization approaches have been retired.

  • Support for MUQ’s multilevel MCMC algorithm was added.

Miscellaneous Enhancements and Bugfixes

  • Enh: Dakota now uses GoogleTest instead of Boost.Test

  • Enh: Dakota now uses std::filesystem when available instead of Boost.filesystem.

  • Enh: The Trilinos snapshot was updated to version 16.1

  • Enh: Switch offline_pilot modes to use oracle covariances when computing control variate parameters, for better consistency in final roll-up between model-based and group-based approaches.

  • Enh: Refactor multilevel_multifidelity_sampling to modernize and improve code reuse

  • Enh: Weighted MLMC sampling now accepts specification of an optimization strategy.

  • Bug fix: In generalized ACV, deduct sunk pilot cost for inactive models from budget when solving for sample allocations for an active subset. In ML BLUE, a similar fix is applied for either inactive models (shared pilot) or inactive groups (independent pilot).

  • Bug fix: In generalized ACV, reset the optimal merit function for each search over model subsets and DAGs (important for online_pilot iteration and model tuning)

  • Bug fix: Hardening of model selection within multilevel_sampling and multifidelity_sampling (hierarchical cases promoted to generalized ACV)

  • Bug fix: Small fixes to JEGA to satisfy recent versions of clang (issues #178 and #94)

  • Bug fix: Boost version check fixed (issue #163)

  • Bug fix: Broken links in examples (issue #162)

Deprecated and Changed

  • The legacy Python interface, which was incompatible with NumPy 2, has been removed.

Compatibility

  • Dakota now requires C++17 and CMake 3.23

  • Legacy Python 2 support has been removed, and Python 3 is required for all optional python dependencies