These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods uncertainty quantification with sampling, reliability, and stochastic finite element methods parameter estimation with nonlinear least squares methods and sensitivity/variance analysis with design of experiments and parameter study methods.
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods.
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