Details
| Original language | English |
|---|---|
| Article number | 115735 |
| Number of pages | 21 |
| Journal | Computer Methods in Applied Mechanics and Engineering |
| Volume | 403 |
| Early online date | 12 Nov 2022 |
| Publication status | Published - 1 Jan 2023 |
Abstract
This work proposes a Bayesian updating approach, called parallel Bayesian optimization and quadrature (PBOQ). It is rooted in Bayesian updating with structural reliability methods (BUS) and offers a coherent Bayesian approach for the BUS analysis by assuming Gaussian process priors. The first step of the method, i.e., parallel Bayesian optimization, effectively explores a constant c in BUS by a novel parallel infill sampling strategy. The second step (parallel Bayesian quadrature) then infers the posterior distribution by another parallel infill sampling strategy using subset simulation. The proposed approach enables to make the fullest use of prior knowledge and parallel computing, resulting in a substantial reduction of the computational burden of model updating. Four numerical examples with varying complexity are investigated for demonstrating the proposed method against several existing methods. The results show the potential benefits by advocating a coherent Bayesian fashion to the BUS analysis.
Keywords
- Bayesian model updating, Bayesian optimization, Bayesian quadrature, Gaussian process, Parallel computing
ASJC Scopus subject areas
- Engineering(all)
- Computational Mechanics
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
- Physics and Astronomy(all)
- General Physics and Astronomy
- Computer Science(all)
- Computer Science Applications
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In: Computer Methods in Applied Mechanics and Engineering, Vol. 403, 115735, 01.01.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Bayesian updating with two-step parallel Bayesian optimization and quadrature
AU - Kitahara, Masaru
AU - Dang, Chao
AU - Beer, Michael
N1 - Funding Information: The first author acknowledges the support of the Deutsche Forschungsgemensschaft (DFG, German Research Foundation) — SFB1463-434502799 . The second author is mainly supported by China Scholarship Council (CSC) . The third author would like to appreciate the support of Sino-German Mobility Program under grant M-0175 and National Natural Science Foundation of China under grant 72271025 .
PY - 2023/1/1
Y1 - 2023/1/1
N2 - This work proposes a Bayesian updating approach, called parallel Bayesian optimization and quadrature (PBOQ). It is rooted in Bayesian updating with structural reliability methods (BUS) and offers a coherent Bayesian approach for the BUS analysis by assuming Gaussian process priors. The first step of the method, i.e., parallel Bayesian optimization, effectively explores a constant c in BUS by a novel parallel infill sampling strategy. The second step (parallel Bayesian quadrature) then infers the posterior distribution by another parallel infill sampling strategy using subset simulation. The proposed approach enables to make the fullest use of prior knowledge and parallel computing, resulting in a substantial reduction of the computational burden of model updating. Four numerical examples with varying complexity are investigated for demonstrating the proposed method against several existing methods. The results show the potential benefits by advocating a coherent Bayesian fashion to the BUS analysis.
AB - This work proposes a Bayesian updating approach, called parallel Bayesian optimization and quadrature (PBOQ). It is rooted in Bayesian updating with structural reliability methods (BUS) and offers a coherent Bayesian approach for the BUS analysis by assuming Gaussian process priors. The first step of the method, i.e., parallel Bayesian optimization, effectively explores a constant c in BUS by a novel parallel infill sampling strategy. The second step (parallel Bayesian quadrature) then infers the posterior distribution by another parallel infill sampling strategy using subset simulation. The proposed approach enables to make the fullest use of prior knowledge and parallel computing, resulting in a substantial reduction of the computational burden of model updating. Four numerical examples with varying complexity are investigated for demonstrating the proposed method against several existing methods. The results show the potential benefits by advocating a coherent Bayesian fashion to the BUS analysis.
KW - Bayesian model updating
KW - Bayesian optimization
KW - Bayesian quadrature
KW - Gaussian process
KW - Parallel computing
UR - http://www.scopus.com/inward/record.url?scp=85141784447&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2022.115735
DO - 10.1016/j.cma.2022.115735
M3 - Article
AN - SCOPUS:85141784447
VL - 403
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0045-7825
M1 - 115735
ER -