Highlight: Generalized Approximate Control Variate Method for Multifidelity Sampling

Dakota can now search over directed acyclic graphs to identify the best model inter-relationships for multifidelity sampling.

Enabling / Accessing:

As part of the approximate_control_variate (ACV) method for multifidelity sampling, the new search_model_graphs option activates the generalized ACV capability that identifies the most performant set of control variate pairings among the models in the multifidelity ensemble.

Documentation:

Refer to DAG recursion types under search_model_graphs.

Highlight: Updated User Resources

Dakota’s website has received a refresh. Documentation has moved to GitHub.io and Dakota downloads are now offered as GitHub Releases.

Enabling / Accessing:

Visit:

• Website: https://dakota.sandia.gov
• Documentation: https://snl-dakota.github.io
• Downloads: https://github.com/snl-dakota/dakota/releases/

See the release notes for further details.