Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 User's Manual
Brian M. Adams, William Bohnhoff, Keith Dalbey, Mohamed S. Ebeida, John Paul Eddy, Michael S Eldred, Russell Hooper, Patricia Hough, Kenneth T. Hu, John Davis Jakeman, Mohammad Khalil, Kathryn A. Maupin, Jason Monschke, Elliott Ridgway, Ahmad Rushdi, Daniel T. Seidl, J.J. Stephens, Justin G. Winokur · 2021
The Dakota 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.