Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (User's Manual)

Citations Over Time

Abstract

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 quantication 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 users manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

Related Papers

• Analysis on parameter sensitivity of distributed hydrological model based on LH-OAT Method(2012)
• On the Relationship between e-sensitivity Analysis and Sensitivity Analysis using an Optimal Basis(2004)
• Sensitivity study on parameters of groundwater thermal transport modeling for GWHP system(2010)
• Experimental on evaluation of reservoir sensitivity(2011)
• → Sensitive vPSA -- Exploring Sensitivity in Visual Parameter Space Analysis(2022)