Skip to Main content Skip to Navigation

Novel algorithm using Active Metamodel Learning and Importance Sampling: application to multiple failure regions of low probability

Nassim Razaaly 1 Pietro Marco Congedo 1 
1 CARDAMOM - Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
Abstract : Calculation of tail probabilities is of fundamental importance in several domains, such as for example risk assessment. One major challenge consists in the computation of low-failure probability and multiple-failure regions, especially when an unbiased estimation of the error is required. Methods developed in literature rely mostly on the construction of an adaptive surrogate, tackling some problems such as the metamodel building criterion and the global computational cost, at the price of a generally biased estimation of the failure probability. In this paper, we propose a novel algorithm permitting to both building an accurate metamodel and to provide a statistically consistent error. In fact, it relies on a novel metamodel building strategy, which aims to refine the limit-state region in all the branches "equally", even in the case of multiple failure regions, with a robust stopping building criterion. Secondly, two "quasi-optimal" importance sampling techniques are used, which permit, by exploiting the accurate knowledge of the metamodel, to provide an unbiased estimation of the failure probability, even if the metamodel is not fully accurate. As a consequence, the proposed method provides a very accurate unbiased estimation even for low failure probability or multiple failure regions. Several numerical examples are carried out, showing the very good performances of the proposed method with respect to the state-of-the-art in terms of accuracy and computational cost. Additionally, another importance sampling technique is proposed in this paper, permitting to drastically reduce the computational cost when estimating some reference values, or when a very weak failure-probability event should be computed directly from the metamodel.
Complete list of metadata

Cited literature [18 references]  Display  Hide  Download
Contributor : Pietro Marco Congedo Connect in order to contact the contributor
Submitted on : Thursday, June 29, 2017 - 5:40:31 PM
Last modification on : Wednesday, February 2, 2022 - 3:54:35 PM
Long-term archiving on: : Monday, January 22, 2018 - 7:05:11 PM


Files produced by the author(s)


  • HAL Id : hal-01550770, version 1



Nassim Razaaly, Pietro Marco Congedo. Novel algorithm using Active Metamodel Learning and Importance Sampling: application to multiple failure regions of low probability. [Research Report] RR-9079, INRIA Bordeaux, équipe CARDAMOM. 2017, pp.30. ⟨hal-01550770⟩



Record views


Files downloads