Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User's Manual
Year: 2020
Type: report
Abstract: The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification 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 flexible 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.
DOI: 10.2172/1630694
Authors: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States), Brian Adams, USDOE National Nuclear Security Administration (NNSA), Sandia National Laboratories, Livermore, CA, William Bohnhoff, Keith Dalbey, Mohamed Ebeida, John Eddy, Michael Eldred, Russell Hooper, Patricia Hough, Kenneth Hu, John Jakeman, Mohammad Khalil, Kathryn Maupin, Jason Monschke, Elliott Ridgway, Ahmad Rushdi, Daniel Seidl, John Stephens, Laura Swiler, Justin Winokur
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4211223617