Released: November 15, 2016
- Specification of linear constraints has been moved from the method block of the Dakota input file to the variables block. The keywords themselves remain unchanged.
- Substantial improvements to active subspace methods for input parameter dimension reduction.
- Considerable improvements in multi-level Monte Carlo, control variate Monte Carlo, and multi-level polynomial regression, including improved fault tolerance.
- New Bayesian experimental design capability - calibrate a low-fidelity model by adaptively selecting experimental configurations at which to run a high-fidelity model.
- New interfacing helpers: Python module (dipy) to simplify interfacing Dakota with Python-based simulations and a Bash script to export Dakota parameters as environment variables.
- The first beta release of the new Dakota GUI is now available for download.