Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :
Sandia National Laboratories (SNL), Albuquerque, NM (United States). New Mexico Small Business Assistance (NMSBA) Program, NM (United States), Brian Adams, USDOE Assistant Secretary for Human Resources and Administration, Mohamed Ebeida, Michael S Eldred, John D. Stephens, Laura Swiler, John Stephens, Dena M. Vigil, Timothy Wildey, William Bohnhoff, John Paul Eddy, Kenneth Hu, Keith Dalbey, Lara E Bauman, Patricia Hough · 2014
The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion 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 surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.