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

verfasst von: Augustin Persoons, Pengfei Wei, Matteo Broggi, Michael Beer
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.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
Externe Organisation(en): KU Leuven
Northwestern Polytechnical University
Typ: Artikel
Journal: Structural and Multidisciplinary Optimization
Band: 66
ISSN: 1615-147X
Publikationsdatum: 23.06.2023
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Software, Steuerungs- und Systemtechnik, Angewandte Informatik, Computergrafik und computergestütztes Design, Steuerung und Optimierung
Elektronische Version(en): https://doi.org/10.1007/s00158-023-03598-6 (Zugang: Geschlossen)
https://livrepository.liverpool.ac.uk/id/eprint/3171699 (Zugang: Offen)