A new scheme combining adaptive Kriging with adaptative variance-reduction using Gaussian mixture importance sampling

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autoren

  • A. Persoons
  • P. Wei
  • L. Bogaerts
  • D. Moens
  • M. Broggi
  • M. Beer

Externe Organisationen

  • KU Leuven
  • Northwestern Polytechnical University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksProceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics
Herausgeber/-innenW. Desmet, B. Pluymers, D. Moens, S. Neeckx
Seiten4985-4998
Seitenumfang14
ISBN (elektronisch)9789082893151
PublikationsstatusVeröffentlicht - 25 Jan. 2023
VeranstaltungISMA2022 - International Conference on Noise and Vibration Engineering; USD2022 - International Conference on Uncertainty in Structural Dynamics - Leuven, Belgien
Dauer: 12 Sept. 202214 Sept. 2022

Publikationsreihe

NameProceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics

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.

ASJC Scopus Sachgebiete

Zitieren

A new scheme combining adaptive Kriging with adaptative variance-reduction using Gaussian mixture importance sampling. / Persoons, A.; Wei, P.; Bogaerts, L. et al.
Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics. Hrsg. / W. Desmet; B. Pluymers; D. Moens; S. Neeckx. 2023. S. 4985-4998 (Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Persoons, A, Wei, P, Bogaerts, L, Moens, D, Broggi, M & Beer, M 2023, A new scheme combining adaptive Kriging with adaptative variance-reduction using Gaussian mixture importance sampling. in W Desmet, B Pluymers, D Moens & S Neeckx (Hrsg.), Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics. Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics, S. 4985-4998, ISMA2022 - International Conference on Noise and Vibration Engineering; USD2022 - International Conference on Uncertainty in Structural Dynamics, Leuven, Belgien, 12 Sept. 2022. https://doi.org/10.5281/zenodo.7568688
Persoons, A., Wei, P., Bogaerts, L., Moens, D., Broggi, M., & Beer, M. (2023). A new scheme combining adaptive Kriging with adaptative variance-reduction using Gaussian mixture importance sampling. In W. Desmet, B. Pluymers, D. Moens, & S. Neeckx (Hrsg.), Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics (S. 4985-4998). (Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics). https://doi.org/10.5281/zenodo.7568688
Persoons A, Wei P, Bogaerts L, Moens D, Broggi M, Beer M. A new scheme combining adaptive Kriging with adaptative variance-reduction using Gaussian mixture importance sampling. in Desmet W, Pluymers B, Moens D, Neeckx S, Hrsg., Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics. 2023. S. 4985-4998. (Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics). doi: 10.5281/zenodo.7568688
Persoons, A. ; Wei, P. ; Bogaerts, L. et al. / A new scheme combining adaptive Kriging with adaptative variance-reduction using Gaussian mixture importance sampling. Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics. Hrsg. / W. Desmet ; B. Pluymers ; D. Moens ; S. Neeckx. 2023. S. 4985-4998 (Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics).
Download
@inproceedings{8d755ce1926d44a6abf74dec5ec7000a,
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",
year = "2023",
month = jan,
day = "25",
doi = "10.5281/zenodo.7568688",
language = "English",
series = "Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics",
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",
note = "ISMA2022 - International Conference on Noise and Vibration Engineering; USD2022 - International Conference on Uncertainty in Structural Dynamics ; Conference date: 12-09-2022 Through 14-09-2022",

}

Download

TY - GEN

T1 - A new scheme combining adaptive Kriging with adaptative variance-reduction using Gaussian mixture importance sampling

AU - Persoons, A.

AU - Wei, P.

AU - Bogaerts, L.

AU - Moens, D.

AU - Broggi, M.

AU - Beer, M.

PY - 2023/1/25

Y1 - 2023/1/25

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85195910094&partnerID=8YFLogxK

U2 - 10.5281/zenodo.7568688

DO - 10.5281/zenodo.7568688

M3 - Conference contribution

AN - SCOPUS:85195910094

T3 - Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics

SP - 4985

EP - 4998

BT - Proceedings of ISMA 2022 - International Conference on Noise and Vibration Engineering and USD 2022 - International Conference on Uncertainty in Structural Dynamics

A2 - Desmet, W.

A2 - Pluymers, B.

A2 - Moens, D.

A2 - Neeckx, S.

T2 - ISMA2022 - International Conference on Noise and Vibration Engineering; USD2022 - International Conference on Uncertainty in Structural Dynamics

Y2 - 12 September 2022 through 14 September 2022

ER -

Von denselben Autoren