@inproceedings{0fb8bc5fe8044b9f90fd0f5666f95585,
title = "A new scheme combining adaptive Kriging with adaptative variance-reduction using Gaussian mixture importance sampling",
abstract = "This article describes a new adaptive Kriging method designed to alleviate the limitations of other related approaches encounter in cases of extremely rare failure events. The main idea is to iteratively reduce both surrogate modelling error and sampling error. To do so the adaptive Kriging framework is associated with the multiple adaptive importance sampling scheme where the auxiliary distribution is iteratively built as a near-optimal Gaussian mixture. The estimator associated with he Gaussian mixture importance sampling is given as well as a stopping criterion based on both the estimated sampling and modelling error. The performances are finally illustrated on two benchmark problems.",
author = "A. Persoons and P. Wei and L. Bogaerts and D. Moens and M. Broggi and M. Beer",
note = "Publisher Copyright: {\textcopyright} 2022 Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics. All rights reserved.; 30th International Conference on Noise and Vibration Engineering, ISMA 2022 and 9th International Conference on Uncertainty in Structural Dynamics, USD 2022 ; Conference date: 12-09-2022 Through 14-09-2022",
year = "2022",
language = "英语",
series = "Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics",
publisher = "KU Leuven, Departement Werktuigkunde",
pages = "4985--4998",
editor = "W. Desmet and B. Pluymers and D. Moens and S. Neeckx",
booktitle = "Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics",
}