A publication of the Advanced Simulation & Computing Division, NA-121.2, NNSA Defense Programs
December 2008
NA-ASC-500-08—Issue 9
Return to this issue’s stories
Latest version of DAKOTA Released
Version 4.2 of the DAKOTA software toolkit was released and deployed in November 2008, and offers substantial advancements that enable efficient, robust analysis and design of critical systems in the presence of uncertainty (illustrated in the attached plot).
DAKOTA, which is currently in use at Sandia, Los Alamos, and Lawrence Livermore National Laboratories to support the nuclear weapon stockpile stewardship program and other national security programs, is also used broadly by academic, government, and corporate institutions for sensitivity analysis, uncertainty quantification, parameter estimation, and design optimization studies. (see: http://www.cs.sandia.gov/dakota)
Specific algorithmic improvements include:
• Uncertainty quantification: new stochastic collocation method based on Lagrange polynomial interpolation and more scalable generalized polynomial chaos methods, extended Latin hypercube sampling distributions and incremental random sampling;
• Optimization: new bi-level, sequential, and multi-fidelity optimization under uncertainty algorithms based on stochastic collocation and polynomial chaos, generalization of efficient global optimization technique;
• Calibration: new capability for surrogate-based model calibration, improved support for model calibration under uncertainty and weighted nonlinear least squares;
• Framework: new radial basis function and moving least squares surrogates, more efficient evaluation cache, and model recursion refinements.
DAKOTA 4.2 provides significant usability improvements, including a newly designed input parser, additional method tutorials, and examples demonstrating coupling DAKOTA to parallel simulation codes for analysis. These examples will be used in upcoming training classes at several sites. There is also improved platform support for Macintosh and Windows.
Finally, Version 4.2 allows more convenient and robust integration into other software libraries, such as Trilinos and Xyce, with special emphasis on efficiency for large-scale applications.
DAKOTA’s stochastic expansion methods can resolve response probability distributions considerably more efficiently than traditional sampling-based approaches. For the “log ratio” benchmark problem, the plot depicts exponential convergence for polynomial chaos approximations constructed with advanced multi-dimensional integration techniques (sparse grid, quadrature, and regression), compared with √n convergence typical of Latin hypercube sampling. These advanced methods will be deployed for UQ and QMU with ASC Full-System Models.
DOE Privacy Disclaimer | Sandia Privacy Disclaimer | 2009-0056 W
Developed and maintained by Sandia National Laboratories for NA-121.2