Announcing Dakota version 6.23

Dakota 6.23 is officially available for download.

Highlight: Compute statistics from imported samples

Version 6.23 includes improved support for computing statistics from imported samples. The import_points method reads samples from a tabular file and computes moments, correlation coefficients, and, optionally, level mappings and Sobol indices.

Enabling / Accessing: The import_samples method is available in all builds of Dakota.

Documentation:

The keyword documentation for the import_points method has further details.

Highlight: Import methods from Python

Dakota can now import and use methods written in Python. Users can implement or wrap their own iterative, black-box algorithms such as optimizers or UQ methods and use them in Dakota studies. Dakota provides the imported method with a wrapped Model instance that it can use to evaluate the function, gradient, and Hessian of responses and perform other operations such as sending results to Dakota’s output stream.

Enabling / Accessing: The external_python method is available in all builds of Dakota.

Documentation:

The keyword documentation for the external_python method has further details.

Full Release Notes

This release features numerous other enhancements to existing capabilities and bugfixes. Full release notes are available at Dakota’s external documentation site, https://snl-dakota.github.io/.