Released: May 15, 2020
As a forward-looking release, Dakota 6.12 highlights several nascent algorithm capabilities that will lead to improved method concurrency, advanced surrogate-based optimization and UQ, including Bayesian inference, and more flexible use of surrogates in meta-methods and workflows. These new features labeled experimental are lightly tested and available to early adopters to use with care.
The 6.12 release also features a number of bug fixes and enhancements, notably improved batch parallel operations, new plotting and other features in the graphical user interface, and more flexible specification of specialized surrogates as models vs. methods.
- The efficient_global method for optimization and least squares now supports concurrent refinement (adding multiple points).
- (Experimental) The MIT Uncertainty Quantification (MUQ) MUQ2 library (Parno, Davis, Marzouk, et al.) enhances Dakota's Bayesian inference capability with new Markov Chain Monte Carlo (MCMC) sampling methods. MCMC samplers available in Dakota (under method > bayes_calibration > muq) include Metropolis-Hastings and Adaptive Metropolis. Future work will activate MUQ's more advanced samplers, including surrogate-based and derivative-enhanced sampling, as well as delayed rejection schemes.
- (Experimental) Dakota 6.12 extends functional tensor train (FTT) surrogate models from the C3 library (Gorodetsky, University of Michigan) to support building FTT approximations across a sequence of model fidelities (multifidelity FTT) or model resolutions (multilevel FTT).
Comprehensive release notes are available at Dakota 6.12 Release Notes