Probabilistic calibration of model parameters with approximate Bayesian quadrature and active machine learning

Pengfei Wei; Masaru Kitahara; Matthias G R Faes; Michael Beer

doi:10.1088/3050-2454/ad9f62

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Probabilistic calibration of model parameters with approximate Bayesian quadrature and active machine learning

Papers | 更新时间：2025-08-22

- Probabilistic calibration of model parameters with approximate Bayesian quadrature and active machine learning
- 基于近似贝叶斯求积与主动机器学习的模型参数概率校准
- “In the field of reliability engineering, Bayesian model updating (BMU) is a crucial challenge. Expert researchers have developed a family of new acquisition functions with closed-form expressions to accelerate the approximate Bayesian quadrature for addressing the BMU problem with the desired level of accuracy. This provides a better trade-off between exploration and exploitation, achieving high accuracy and efficiency.”
- Journal of Reliability Science and Engineering Vol. 1, Issue 1, Pages: 80-101(2025)
- 作者机构：
  
  School of Power and Energy, Northwestern Polytechnical University, Xi'an 710072, People's Republic of China
  Advanced Power Research Institute of Northwestern Polytechnical University, Chengdu, Sichuan, People's Republic of China
  Department of Civil Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan
  Chair for Reliability Engineering, TU Dortmund University, Leonhard-Euler Strasse 5, 44227Dortmund, Germany
  Institute for Risk and Reliability, Leibniz University Hannover, Callinstr. 34, Hannover 30167, Germany
  Department of Civil and Environmental Engineering, University of Liverpool, Liverpool L69 3BX, United Kingdom
  International Joint Research Center for Resilient Infrastructure & International Joint Research Center for Engineering Reliability and Stochastic Mechanics, Tongji University, Shanghai 200092, People's Republic of China
- 作者简介：
  
  [ "Assoc. Prof. Pengfei Wei is an experienced principal investigator, in the directions of Bayesian intelligent computing, model VV&UQ, and reliability engineering, and is affiliated to the school of power and energy of Northwestern Polytechnical University, China." ]
  [ "Dr. Masaru Kitahara is Assistant Professor at Department of Civil Engineering of the University of Tokyo and works in the Bridge and Wind Engineering Laboratory." ]
  [ "Prof. Dr. Matthias Faes is head of the Chair for Reliability Engineering and full professor at the mechanical engineering department of TU Dortmund University." ]
  [ "Prof. Dr. Michael Beer is the head of the Institute for Risk and Reliability at Leibniz University Hannover as well as part-time Professor of Uncertainty in Engineering in the University of Liverpool, UK and a Guest Professor at Tongji University, China." ]
- 基金信息：
- DOI：10.1088/3050-2454/ad9f62
  CLC：
- Received：21 October 2024，
  
  Revised：2024-11-19，
  
  Accepted：09 December 2024，
  
  Published Online：22 January 2025，
  
  Published：2025-03
- 稿件说明：
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Pengfei Wei, Masaru Kitahara, Matthias G R Faes, et al. Probabilistic Calibration of Model Parameters with Approximate Bayesian Quadrature and Active Machine Learning[J]. Journal of Reliability Science and Engineering, 2025, 1: 015003.

Pengfei Wei, Masaru Kitahara, Matthias G R Faes, et al. 基于近似贝叶斯求积与主动机器学习的模型参数概率校准[J]. 可靠性科学与工程学报（英文）, 2025, 1: 015003.
Pengfei Wei, Masaru Kitahara, Matthias G R Faes, et al. Probabilistic Calibration of Model Parameters with Approximate Bayesian Quadrature and Active Machine Learning[J]. Journal of Reliability Science and Engineering, 2025, 1: 015003. DOI： 10.1088/3050-2454/ad9f62.

Pengfei Wei, Masaru Kitahara, Matthias G R Faes, et al. 基于近似贝叶斯求积与主动机器学习的模型参数概率校准[J]. 可靠性科学与工程学报（英文）, 2025, 1: 015003. DOI： 10.1088/3050-2454/ad9f62.

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Probabilistic calibration of model parameters with approximate Bayesian quadrature and active machine learning

DOI：10.1088/3050-2454/ad9f62