Version 6.24 (2026/05/15)

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.

Highlight: 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.

Improvements by Category

Interfaces, Input/Output

UQ Methods

  • Enhancements to numerical solutions within multifidelity Monte Carlo (MFMC) implementation: fix any remaining model order dependencies when using the default reorder-on-the-fly approach (auto_reorder), and add an option to enable solutions for a specified model ordering by enforcing ordered sample allocations through linear constraints (fixed_order).

Sensitivity Analysis

  • The binned approach to variance-based decomposition now works with discrete variables.

Miscellaneous Enhancements and Bugfixes

  • Fix issue with trilinos that prevented re-configuration in the same build directory

Deprecated and Changed

  • Dakota’s NIDR (New Input Deck Reader) is being deprecated and replaced with a newly written input parser. For this release, NIDR is available as a fallback. Use the command line argument -parser legacy to use it.

Compatibility

  • The schema for JSON input files is defined by Pydantic models, which are located at python/dakota/spec/ in the source tree. To update Dakota’s input grammar, Python >=3.9 and Pydantic >=2.12 are required.