Announcing Dakota version 6.24

Dakota 6.24 is officially available for download.

Highlight: Sensitivity Analysis Section

A new section that describes Dakota’s sensitivity analysis capabilities has been added to the User Manual.

Enabling / Accessing: Visit Sensitivity Analysis.

JSON-format Input Files

In addition to the existing freeform input file format, Dakota now accepts JSON-format input files. The schema and other usage tips are described in the JSON Input Reference section. JSON input provides a foundation for more structured, tool-friendly workflows, and is a stepping stone toward more capable Python bindings.

The capability is experimental, and we appreciate your bug reports. To fall back to the old input file reader, run Dakota with the -parser nidr command line argument.

Enabling / Accessing: Write a JSON format input file and use the -json command line argument (e.g. dakota -json dakota_in.json).

Highlight: Robustness enhancements for multifidelity sampling

All multifidelity sampling estimators that involve numerical solutions for sample allocations (MFMC, ACV, parameterized ACV, and MLBLUE) now include analytic derivatives of all accuracy and cost metrics, rendering these allocations more accurate and numerically robust.

Enabling / Accessing: no additional specification is required, as all numerical solutions have been promoted to analytic gradients.

Documentation: multifidelity sampling methods that support/require numerical solutions are described at multifidelity_sampling, approximate_control_variate, and multilevel_blue.

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/.