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Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes

Published on Dec 1, 2017in Reliability Engineering & System Safety4.04
· DOI :10.1016/j.ress.2017.11.022
Bertrand Iooss15
Estimated H-index: 15
(Institut de Mathématiques de Toulouse),
Loic Le Gratiet6
Estimated H-index: 6
(Environmental Defense Fund)
Abstract
A functional risk curve gives the probability of an undesirable event as a function of the value of a critical parameter of a considered physical system. In several applicative situations, this curve is built using phenomenological numerical models which simulate complex physical phenomena. To avoid cpu-time expensive numerical models, we propose to use Gaussian process regression to build functional risk curves. An algorithm is given to provide confidence bounds due to this approximation. Two methods of global sensitivity analysis of the models' random input parameters on the functional risk curve are also studied. In particular, the PLI sensitivity indices allow to understand the effect of misjudgment on the input parameters' probability density functions.
  • References (37)
  • Citations (5)
References37
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7 CitationsSource
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20 CitationsSource
#1Bertrand Iooss (Institut de Mathématiques de Toulouse)H-Index: 15
#2Paul Lemaître (IRIA: French Institute for Research in Computer Science and Automation)H-Index: 2
203 CitationsSource
Cited By5
Newest
#1Xiaosong Du (Iowa State University)H-Index: 3
#2Leifur Leifsson (Iowa State University)H-Index: 15
Source
#1Xiaosong Du (Iowa State University)H-Index: 3
#2Leifur Leifsson (Iowa State University)H-Index: 15
Last.Ronald A. Roberts (Iowa State University)H-Index: 10
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1 CitationsSource
1 CitationsSource
#1Maria Steiner (Bauhaus University, Weimar)H-Index: 2
#2Jean-Marc BourinetH-Index: 8
Last.Tom Lahmer (Bauhaus University, Weimar)H-Index: 17
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