Dakota: Optimization and Uncertainty Quantification Algorithms for Design Exploration and Simulation Credibility.

The Dakota toolkit provides a flexible, extensible interface between analysis codes and iterative systems analysis methods. Dakota contains algorithms for:

·         optimization with gradient and nongradient-based methods;

·         uncertainty quantification with sampling, reliability, stochastic expansion, and epistemic methods;

·         parameter estimation with nonlinear least squares methods; and

·         sensitivity/variance analysis with design of experiments and parameter study methods.

These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty.

Associated Projects: Albany  ALEGRA  CASL  FASTMath  Kokkos  Trilinos  

Software website

Contact: Adams, Brian M., briadam@sandia.gov
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