Dakota 6.14 has been released. See the release notes for further details. A few of the highlights include:

Highlight: Examples Library

Dakota is launching a new Examples Library with the 6.14 release. The library includes many brand new and refreshed examples that demonstrate a broad range of tried-and-true and cutting-edge Dakota capabilities. Tutorials, runnable Dakota studies, drivers, case studies, and more are available. Each example is accompanied by a detailed README that includes a helpful description, how to run the example, additional resources, and other information.

The Dakota team welcomes community contributions to the Examples Library. Please contact us through the dakota-users mailing list if you have something to add.

Enabling / Accessing: In Dakota binary packages, the Examples Library is located in the share/dakota/examples/official folder. In source packages, they are in the dakota-examples folder. The Dakota project soon will make the Library available on the web. Sign up for dakota-announce to be notified.

Highlight: Batch-parallel EGO

Efficient global optimization (EGO) is a popular choice for global optimization in Dakota, and users have often requested greater parallel concurrency to better balance the initial Gaussian process model construction and subsequent refinement. Dakota now includes batch-parallel EGO capabilities, with support for blocking and non-blocking approaches and refinement candidate identification from a combination of acquisition and exploration. See some benchmark results and additional details provided below. 

Enabling / Accessing: The efficient_global method now admits batch size and parallel synchronization controls in the user input file.

Documentation: Refer to efficient_global method documentation in the Dakota 6.14 Reference Manual.

Highlight: Surrogate Workflows

Dakota 6.14 features several surrogate model enhancements. Select surrogate models can be exported from Dakota for subsequent reuse in Dakota itself, via the Python dakota.surrogates modules, or as integrated workflow nodes in the GUI, allowing them to be built once and then used as stand-ins for the simulation models they approximate. Experimental Gaussian Process models now feature improved default options, better integration in GP-based meta-methods, and finer-grained user control via advanced options.

Enabling / Accessing: New input file keywords enable surrogate export/import and additional power-user controls. The GUI has new special treatment of surrogate models, including a Surrogate block on the workflow palette.

Documentation: See the Dakota 6.14 Reference Manual, GUI Manual, and the official/surrogates/ directory in the aforementioned Examples Library for details on use.

Highlight: Embedded cross validation for greedy refinement

Various surrogate-based UQ methods rely on regression to solve for approximation coefficients. This release includes new cross-validation capabilities for functional tensor train (FTT) and polynomial chaos expansion (PCE) surrogates that are based on regression. This is motivated by greedy refinement approaches, whether single-fidelity or multifidelity, where it is critical that refinement candidates be greedily selected for high impact due to the right reasons (improved accuracy) rather than the wrong reasons (overfitting). Additional details (refinement saturation, candidate throttling, response scaling) are provided below.

Enabling / Accessing: Cross-validation can be activated for both FTT and PCE, across both rank and basis order for FTT and across both basis order and noise tolerance for PCE. For PCE, the systems may be either overdetermined (ordinary least squares) or under-determined (compressed sensing).

Documentation: Within the Dakota 6.14 Reference Manual, refer to cross_validation documentation for PCE and adapt_{rank,order} documentation for FTT.