Dakota

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.

Software Website

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Contact
Brian M. Adams, briadam@sandia.gov