Combining data and physical models for probabilistic analysis: A Bayesian Augmented Space Learning perspective

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Fangqi Hong
  • Pengfei Wei
  • Jingwen Song
  • Matthias G.R. Faes
  • Marcos A. Valdebenito
  • Michael Beer

Externe Organisationen

  • Northwestern Polytechnical University
  • Technische Universität Dortmund
  • The University of Liverpool
  • International Joint Research Center for Engineering Reliability and Stochastic Mechanics
  • Tongji University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer103474
FachzeitschriftProbabilistic Engineering Mechanics
Jahrgang73
PublikationsstatusVeröffentlicht - Juli 2023

Abstract

The traditional methods for probabilistic analysis of physical systems often follow a non-intrusive scheme with, random samples for stochastic model parameters generated in the outer loop, and for each sample, physical model (described by PDEs) solved in the inner loop using, e.g., finite element method (FEM). Two of the biggest challenges when applying probabilistic methods are the high computational burden due to the repeated calls of the expensive-to-estimate computational models, and the difficulties of integrating the numerical errors from both loops. To overcome these challenges, we present a new framework for transforming the PDEs with stochastic parameters into equivalent deterministic PDEs, and then devise a statistical inference method, called Bayesian Augmented Space Learning (BASL), for inferring the probabilistic descriptors of the model responses with the combination of measurement data and physical models. With the two sources of information available, only a one-step Bayesian inference needs to be performed, and the numerical errors are summarized by posterior variances. The method is then further extended to the case where the values of the parameters of the test pieces for measurement are not precisely known. The effectiveness of the proposed methods is demonstrated with academic and real-world physical models.

ASJC Scopus Sachgebiete

Zitieren

Combining data and physical models for probabilistic analysis: A Bayesian Augmented Space Learning perspective. / Hong, Fangqi; Wei, Pengfei; Song, Jingwen et al.
in: Probabilistic Engineering Mechanics, Jahrgang 73, 103474, 07.2023.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Hong F, Wei P, Song J, Faes MGR, Valdebenito MA, Beer M. Combining data and physical models for probabilistic analysis: A Bayesian Augmented Space Learning perspective. Probabilistic Engineering Mechanics. 2023 Jul;73:103474. doi: 10.1016/j.probengmech.2023.103474
Download
@article{d77ef02b136840b0b501ff41a769a3e0,
title = "Combining data and physical models for probabilistic analysis: A Bayesian Augmented Space Learning perspective",
abstract = "The traditional methods for probabilistic analysis of physical systems often follow a non-intrusive scheme with, random samples for stochastic model parameters generated in the outer loop, and for each sample, physical model (described by PDEs) solved in the inner loop using, e.g., finite element method (FEM). Two of the biggest challenges when applying probabilistic methods are the high computational burden due to the repeated calls of the expensive-to-estimate computational models, and the difficulties of integrating the numerical errors from both loops. To overcome these challenges, we present a new framework for transforming the PDEs with stochastic parameters into equivalent deterministic PDEs, and then devise a statistical inference method, called Bayesian Augmented Space Learning (BASL), for inferring the probabilistic descriptors of the model responses with the combination of measurement data and physical models. With the two sources of information available, only a one-step Bayesian inference needs to be performed, and the numerical errors are summarized by posterior variances. The method is then further extended to the case where the values of the parameters of the test pieces for measurement are not precisely known. The effectiveness of the proposed methods is demonstrated with academic and real-world physical models.",
keywords = "Augmented space, Bayesian learning, Gaussian process regression, Parameter identification, Probabilistic analysis",
author = "Fangqi Hong and Pengfei Wei and Jingwen Song and Faes, {Matthias G.R.} and Valdebenito, {Marcos A.} and Michael Beer",
note = "Funding Information: Pengfei Wei acknowledges the supports of the National Natural Science Foundation of China under grant number 72171194 and the Sino-German Mobility Programme under grant number M-0175 (2021–2023). Matthias Faes acknowledges the support of the Research Foundation Flanders (FWO), Belgium under grant 12P3519N , as well as of the Humboldt foundation.",
year = "2023",
month = jul,
doi = "10.1016/j.probengmech.2023.103474",
language = "English",
volume = "73",
journal = "Probabilistic Engineering Mechanics",
issn = "0266-8920",
publisher = "Elsevier Ltd.",

}

Download

TY - JOUR

T1 - Combining data and physical models for probabilistic analysis

T2 - A Bayesian Augmented Space Learning perspective

AU - Hong, Fangqi

AU - Wei, Pengfei

AU - Song, Jingwen

AU - Faes, Matthias G.R.

AU - Valdebenito, Marcos A.

AU - Beer, Michael

N1 - Funding Information: Pengfei Wei acknowledges the supports of the National Natural Science Foundation of China under grant number 72171194 and the Sino-German Mobility Programme under grant number M-0175 (2021–2023). Matthias Faes acknowledges the support of the Research Foundation Flanders (FWO), Belgium under grant 12P3519N , as well as of the Humboldt foundation.

PY - 2023/7

Y1 - 2023/7

N2 - The traditional methods for probabilistic analysis of physical systems often follow a non-intrusive scheme with, random samples for stochastic model parameters generated in the outer loop, and for each sample, physical model (described by PDEs) solved in the inner loop using, e.g., finite element method (FEM). Two of the biggest challenges when applying probabilistic methods are the high computational burden due to the repeated calls of the expensive-to-estimate computational models, and the difficulties of integrating the numerical errors from both loops. To overcome these challenges, we present a new framework for transforming the PDEs with stochastic parameters into equivalent deterministic PDEs, and then devise a statistical inference method, called Bayesian Augmented Space Learning (BASL), for inferring the probabilistic descriptors of the model responses with the combination of measurement data and physical models. With the two sources of information available, only a one-step Bayesian inference needs to be performed, and the numerical errors are summarized by posterior variances. The method is then further extended to the case where the values of the parameters of the test pieces for measurement are not precisely known. The effectiveness of the proposed methods is demonstrated with academic and real-world physical models.

AB - The traditional methods for probabilistic analysis of physical systems often follow a non-intrusive scheme with, random samples for stochastic model parameters generated in the outer loop, and for each sample, physical model (described by PDEs) solved in the inner loop using, e.g., finite element method (FEM). Two of the biggest challenges when applying probabilistic methods are the high computational burden due to the repeated calls of the expensive-to-estimate computational models, and the difficulties of integrating the numerical errors from both loops. To overcome these challenges, we present a new framework for transforming the PDEs with stochastic parameters into equivalent deterministic PDEs, and then devise a statistical inference method, called Bayesian Augmented Space Learning (BASL), for inferring the probabilistic descriptors of the model responses with the combination of measurement data and physical models. With the two sources of information available, only a one-step Bayesian inference needs to be performed, and the numerical errors are summarized by posterior variances. The method is then further extended to the case where the values of the parameters of the test pieces for measurement are not precisely known. The effectiveness of the proposed methods is demonstrated with academic and real-world physical models.

KW - Augmented space

KW - Bayesian learning

KW - Gaussian process regression

KW - Parameter identification

KW - Probabilistic analysis

UR - http://www.scopus.com/inward/record.url?scp=85173470706&partnerID=8YFLogxK

U2 - 10.1016/j.probengmech.2023.103474

DO - 10.1016/j.probengmech.2023.103474

M3 - Article

AN - SCOPUS:85173470706

VL - 73

JO - Probabilistic Engineering Mechanics

JF - Probabilistic Engineering Mechanics

SN - 0266-8920

M1 - 103474

ER -

Von denselben Autoren