N
Nicolas Gayton
Researcher at University of Auvergne
Publications - 56
Citations - 2833
Nicolas Gayton is an academic researcher from University of Auvergne. The author has contributed to research in topics: Reliability (statistics) & Tolerance analysis. The author has an hindex of 16, co-authored 54 publications receiving 2030 citations. Previous affiliations of Nicolas Gayton include Centre national de la recherche scientifique & Institut Français.
Papers
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AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation
TL;DR: An iterative approach based on Monte Carlo Simulation and Kriging metamodel to assess the reliability of structures in a more efficient way and is shown to be very efficient as the probability of failure obtained with AK-MCS is very accurate and this, for only a small number of calls to the performance function.
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A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models
TL;DR: An original and easily implementable method called AK-IS for active learning and Kriging-based Importance Sampling, based on the AK-MCS algorithm, that enables the correction or validation of the FORM approximation with only a very few mechanical model computations.
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AK-SYS: An adaptation of the AK-MCS method for system reliability
William Fauriat,Nicolas Gayton +1 more
TL;DR: This paper focuses on sampling techniques and, considering the recent adaptation of the EGRA method for systems, a strategy is presented to adapt the AK-MCS method for system reliability.
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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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AK-MCSi: A Kriging-based method to deal with small failure probabilities and time-consuming models
TL;DR: The proposed paper introduces a sequential Monte Carlo Simulation technique to deal with small failure probabilities, and introduces a multipoint enrichment technique to allow parallelization and thus to reduce numerical efforts.