Novel algorithm using active metamodel learning and importance sampling: application to multiple failure regions of low probability

DOI10.1016/J.JCP.2018.04.047zbMath1392.74094OpenAlexW2733267578MaRDI QIDQ725434

Nassim Razaaly, Pietro Marco Congedo

Publication date: 1 August 2018

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/hal-01550770/file/RR-9079.pdf

zbMATH Keywords

tail probabilityimportance samplingrisk analysisunbiased estimationlow failure probabilitymultiple failure regions

Mathematics Subject Classification ID

Reliability and life testing (62N05)

Related Items (2)

Efficient estimation of extreme quantiles using adaptive kriging and importance sampling ⋮ Non-parametric measure approximations for constrained multi-objective optimisation under uncertainty

Uses Software

• GPy
• AK-MCS
• SU2

Cites Work

• Reliability-based structural optimization using neural networks and Monte Carlo simulation
• An efficient reliability method combining adaptive importance sampling and kriging metamodel
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