Challenge problems: uncertainty in system response given uncertain parameters

被引:335
作者
Oberkampf, WL
Helton, JC
Joslyn, CA
Wojtkiewicz, SF
Ferson, S
机构
[1] Sandia Natl Labs, Validat & Uncertainty Estimat Dept, Albuquerque, NM 87185 USA
[2] Arizona State Univ, Dept Math, Tempe, AZ 85287 USA
[3] Los Alamos Natl Lab, Los Alamos, NM 87545 USA
[4] Sandia Natl Labs, Struct Dynam Res Dept, Albuquerque, NM 87185 USA
[5] App Biomath, Moscow 117333, Russia
关键词
epistemic uncertainty; reducible uncertainty; subjective uncertainty; aleatory uncertainty; variability; irreducible uncertainty;
D O I
10.1016/j.ress.2004.03.002
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The risk assessment community has begun to make a clear distinction between aleatory and epistemic uncertainty in theory and in practice. Aleatory uncertainty is also referred to in the literature as variability, irreducible uncertainty, inherent uncertainty, and stochastic uncertainty. Epistemic uncertainty is also termed reducible uncertainty, subjective uncertainty, and state-of-knowledge uncertainty. Methods to efficiently represent, aggregate, and propagate different types of uncertainty through computational models are clearly of vital importance. The most widely known and developed methods are available within the mathematics of probability theory, whether frequentist or subjectivist. Newer mathematical approaches, which extend or otherwise depart from probability theory, are also available, and are sometimes referred to as generalized information theory (GIT). For example, possibility theory, fuzzy set theory, and evidence theory are three components of GIT. To try to develop a better understanding of the relative advantages and disadvantages of traditional and newer methods and encourage a dialog between the risk assessment, reliability engineering, and GIT communities, a workshop was held. To focus discussion and debate at the workshop, a set of prototype problems, generally referred to as challenge problems, was constructed. The challenge problems concentrate on the representation, aggregation, and propagation of epistemic uncertainty and mixtures of epistemic and aleatory uncertainty through two simple model systems. This paper describes the challenge problems and gives numerical values for the different input parameters so that results from different investigators can be directly compared. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:11 / 19
页数:9
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