Efficiency comparison of MCMC and Transport Map Bayesian posterior estimation for structural health monitoring

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External Research Organisations

  • École normale supérieure Paris-Saclay (ENS Paris-Saclay)
  • University of Liverpool
  • Tongji University
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Details

Original languageEnglish
Article number111440
Number of pages16
JournalMechanical Systems and Signal Processing
Volume216
Early online date30 Apr 2024
Publication statusPublished - 1 Jul 2024

Abstract

In this paper, an alternative to solving Bayesian inverse problems for structural health monitoring based on a variational formulation with so-called transport maps is examined. The Bayesian inverse formulation is a widely used tool in structural health monitoring applications. While Markov Chain Monte Carlo (MCMC) methods are often implemented in these settings, they come with the problem of using many model evaluations, which in turn can become quite costly. We focus here on recent developments in the field of transport theory, where the problem is formulated as finding a deterministic, invertible mapping between some easy to evaluate reference density and the posterior. The resulting variational formulation can be solved with integration and optimization methods. We develop a general formulation for the application of transport maps to vibration-based structural health monitoring. Further, we study influences of different integration approaches on the efficiency and accuracy of the transport map approach and compare it to the Transitional MCMC algorithm, a widely used method for structural identification. Both methods are applied to a lower-dimensional dynamic model with uni- and multi-modal properties, as well as to a higher-dimensional neural network surrogate system of an airplane structure. We find that transport maps have a significant increase in accuracy and efficiency, when used in the right circumstances.

Keywords

    Bayesian updating, Markov chain Monte Carlo, Structural health monitoring, Transport maps, Variational inference

ASJC Scopus subject areas

Cite this

Efficiency comparison of MCMC and Transport Map Bayesian posterior estimation for structural health monitoring. / Grashorn, Jan; Broggi, Matteo; Chamoin, Ludovic et al.
In: Mechanical Systems and Signal Processing, Vol. 216, 111440, 01.07.2024.

Research output: Contribution to journalArticleResearchpeer review

Grashorn J, Broggi M, Chamoin L, Beer M. Efficiency comparison of MCMC and Transport Map Bayesian posterior estimation for structural health monitoring. Mechanical Systems and Signal Processing. 2024 Jul 1;216:111440. Epub 2024 Apr 30. doi: 10.1016/j.ymssp.2024.111440
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