Detecting and modeling critical dependence structures between random inputs of computer models

Benoumechiara Nazih (), Bousquet Nicolas (), Michel Bertrand () and Saint-Pierre Philippe ()
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Benoumechiara Nazih: Sorbonne Université, LPSM & EDF R&D
Bousquet Nicolas: Sorbonne Université, LPSM & EDF R&D
Michel Bertrand: École Centrale Nantes
Saint-Pierre Philippe: Institut de Mathématiques de Toulouse, Université Toulouse III

Dependence Modeling, 2020, vol. 8, issue 1, 263-297

Abstract: Uncertain information on input parameters of computer models is usually modeled by considering these parameters as random, and described by marginal distributions and a dependence structure of these variables. In numerous real-world applications, while information is mainly provided by marginal distributions, typically from samples, little is really known on the dependence structure itself. Faced with this problem of incomplete or missing information, risk studies that make use of these computer models are often conducted by considering independence of input variables, at the risk of including irrelevant situations. This approach is especially used when reliability functions are considered as black-box models. Such analyses remain weakened in absence of in-depth model exploration, at the possible price of a strong risk misestimation. Considering the frequent case where the reliability output is a quantile, this article provides a methodology to improve risk assessment, by exploring a set of pessimistic dependencies using a copula-based strategy. In dimension greater than two, a greedy algorithm is provided to build input regular vine copulas reaching a minimum quantile to which a reliability admissible limit value can be compared, by selecting pairwise components of sensitive influence on the result. The strategy is tested over toy models and a real industrial case-study. The results highlight that current approaches can provide non-conservative results.

Keywords: computer model; output quantile; structural reliability; copula; regular vine (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:8:y:2020:i:1:p:263-297:n:12

DOI: 10.1515/demo-2020-0016

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