J
Jean-Marc Bourinet
Researcher at University of Auvergne
Publications - 40
Citations - 1591
Jean-Marc Bourinet is an academic researcher from University of Auvergne. The author has contributed to research in topics: Reliability (statistics) & Kriging. The author has an hindex of 14, co-authored 40 publications receiving 1254 citations. Previous affiliations of Jean-Marc Bourinet include International Facility Management Association & Blaise Pascal University.
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Assessing small failure probabilities by combined subset simulation and Support Vector Machines
TL;DR: A new approach for estimating small failure probabilities by considering subset simulation proposed by S.-K.
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Reliability-based design optimization using kriging surrogates and subset simulation
TL;DR: The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems that remains applicable when the performance models are expensive to evaluate.
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CQ2RS: a new statistical approach to the response surface method for reliability analysis
TL;DR: In this paper, the authors propose a response surface method named CQ2RS (Complete Quadratic Response Surface with ReSampling) allowing to take into account the knowledge of the engineer on one hand and to reduce the cost of the reliability analysis using a statistical formulation of the RSM problem on the other hand.
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Rare-event probability estimation with adaptive support vector regression surrogates
TL;DR: The key idea is to iteratively construct surrogates which quickly explore the safe domain and focus on the limit-state surface in its final stage by minimizing an estimation of the leave-one-out error with the cross-entropy method.
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Quantile-based optimization under uncertainties using adaptive Kriging surrogate models
TL;DR: In this article, a quantile-based approach to solve reliability-based design optimization (RBDO) problems is proposed, where the safety constraints are formulated as admissible probabilities of failure into constraints on quantiles of the performance criteria and the quantile level controls the degree of conservatism of the design.