Publications

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 User's Manual

Adams, Brian M.; Bohnhoff, William B.; Dalbey, Keith D.; Ebeida, Mohamed S.; Eddy, John E.; Eldred, Michael E.; Hooper, Russell H.; Hough, Patricia H.; Hu, Kenneth H.; Jakeman, John J.; Khalil, Mohammad K.; Maupin, Kathryn M.; Monschke, Jason A.; Ridgway, Elliott R.; Rushdi, Ahmad A.; Seidl, Daniel S.; Stephens, John A.; Winokur, Justin W.

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.