We propose a rigorous framework for Uncertainty Quantification (UQ) in which
the UQ objectives and the assumptions/information set are brought to the forefront.
This framework, which we call Optimal Uncertainty Quantification (OUQ), is based
on the observation that, given a set of assumptions and information about the problem,
there exist optimal bounds on uncertainties: these are obtained as extreme
values of well-defined optimization problems corresponding to extremizing probabilities
of failure, or of deviations, subject to the constraints imposed by the scenarios
compatible with the assumptions and information. In particular, this framework
does not implicitly impose inappropriate assumptions, nor does it repudiate relevant
information.
存档附件原文地址
原文发布时间:2010/9/26
引用本文:
Houman Owhadi;Clint Scovel;Timothy John Sullivan.Optimal Uncertainty Quantification .http://ynufe.firstlight.cn/View.aspx?infoid=991679&cb=wxm2010.
发布时间:2010/9/26.检索时间:2024/12/15