Dakota 6.1

Released: November 15, 2014
Release Highlights:

  • New capability for adaptive basis selection for high-dimensional compressed sensing using an expanding front approach.
  • New surrogate-based approach for POFDarts, improving the efficiency of adaptive tail probability estimation.
  • Continuing from the "strategy refactor" phase 1 developments released in v6.0, phase 2 has added new parallel recursion capabilities that support an open-ended number of levels of iterator concurrency within meta-iterators and nested models. Notable studies with enhanced parallelism include design and model calibration under uncertainty and mixed aleatory-epistemic uncertainty quantification.