Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published February 24, 2016 | Submitted
Report Open

The optimal uncertainty algorithm in the mystic framework

Abstract

We have recently proposed a rigorous framework for Uncertainty Quantification (UQ) in which UQ objectives and assumption/information set are brought into the forefront, providing a framework for the communication and comparison of UQ results. In particular, this framework does not implicitly impose inappropriate assumptions nor does it repudiate relevant information. This framework, which we call Optimal Uncertainty Quantification (OUQ), is based on the observation that given a set of assumptions and information, there exist bounds on uncertainties obtained as values of optimization problems and that these bounds are optimal. It provides a uniform environment for the optimal solution of the problems of validation, certification, experimental design, reduced order modeling, prediction, extrapolation, all under aleatoric and epistemic uncertainties. OUQ optimization problems are extremely large, and even though under general conditions they have finite-dimensional reductions, they must often be solved numerically. This general algorithmic framework for OUQ has been implemented in the mystic optimization framework. We describe this implementation, and demonstrate its use in the context of the Caltech surrogate model for hypervelocity impact.

Additional Information

August 21, 2010. (Submitted on 6 Feb 2012). The authors gratefully acknowledge portions of this work supported by the Department of Energy National Nuclear Security Administration under Award Number DE-FC52-08NA28613 and by the National Science Foundation under Award Number DMR-0520547.

Attached Files

Submitted - 1202.1055.pdf

Files

1202.1055.pdf
Files (620.9 kB)
Name Size Download all
md5:2ffe5a06900b83a8e6f0856fb1e3b0d9
620.9 kB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 17, 2023