DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.

Eldred, Michael Scott ; Vigil, Dena M. ; Dalbey, Keith R. ; Bohnhoff, William J. ; Adams, Brian M. ; Swiler, Laura Painton ; Lefantzi, Sophia (Sandia National Laboratories, Livermore, CA) ; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA) ; Edd

关键词: ALGORITHMS;    COMPUTERS;    DESIGN;    NONLINEAR PROGRAMMING;    OPTIMIZATION;    PERFORMANCE;    RELIABILITY;    SAMPLING;    SENSITIVITY ANALYSIS;    SIMULATION;   

DOI  :  10.2172/1031910
RP-ID  :  SAND2011-9106
PID  :  OSTI ID: 1031910
Others  :  TRN: US201202%%69

学科分类：社会科学、人文和艺术（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) 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 theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.