A new reliability method combining adaptive Kriging and active variance reduction using multiple importance sampling

Persoons, Augustin, Wei, Pengfei, Broggi, Matteo and Beer, Michael ORCID: 0000-0002-0611-0345 (2023) A new reliability method combining adaptive Kriging and active variance reduction using multiple importance sampling. Structural and Multidisciplinary Optimization, 66 (6). 144-.

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Abstract

This article describes a new adaptive Kriging method combined with adaptive importance sampling approximating the optimal auxiliary by iteratively building a Gaussian mixture distribution. The aim is to iteratively reduce both the modeling and sampling errors simultaneously, thus avoiding limitations in cases of very rare failure events. At each iteration, a near optimal auxiliary Gaussian distribution is defined and new samples are drawn from it following the scheme of adaptive multiple importance sampling (MIS). The corresponding estimator is provided as well as its variance. A new learning function is developed as a generalization of the U learning function for MIS populations. A stopping criterion is proposed based on both the modeling error and the variance of the estimator. Results on benchmark problems show that the method exhibits very good performances on both efficiency and accuracy.

Item Type:	Article
Uncontrolled Keywords:	Reliability method, Adaptive Kriging, Multiple importance sampling, Extremely rare failure events, Variance reduction
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	18 Jul 2023 09:11
Last Modified:	15 Mar 2024 05:26
DOI:	10.1007/s00158-023-03598-6
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3171699