Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends

[2505.22223] Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Skip to main content Search Submit Donate Log in Physics > Data Analysis, Statistics and Probability arXiv:2505.22223 (physics) [Submitted on 28 May 2025 ( v1 ), last revised 1 Jun 2025 (this version, v2)] Title: Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Authors: Wang-Ji Yan , Lin-Feng Mei , Yuan-Wei Yin , Jiang Mo , Costas Papadimitriou , Ka-Veng Yuen , Michael Beer View PDF Abstract: Bayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis of complex dynamical systems. This review meticulously traces the three-decade evolution of Bayesian learning in structural dynamics, illuminating core principles, groundbreaking methodologies, and diverse applications that have significantly influenced the field. The narrative commences by delving into the basics of Bayesian theory, clarifying essential concepts, and introducing primary methods for deriving posterior distributions, with an in-depth exploration of three types: Laplace approximation, stochastic sampling, and variational inference. Subsequently, the text explores the implementation of two types of Bayesian learning in structural dynamics: physical model learning and data-centric statistical model learning. Physical model learning emphasizes inferring physical model parameters within a Bayesian framework for system identification and prediction, while statistical model learning integrates Bayesian learning methodologies into data-centric statistical modeling within probabilistic machine learning. Both types resonate across various applications, such as modal analysis, model updating, damage detection, and reliability updating, highlighting their pivotal role in enhancing comprehension of dynamical systems and decision-making. The paper also navigates obstacles by proposing ways to enhance existing Bayesian inference strategies. Distinguished from previous research, this study offers a thorough examination of both traditional and cutting-edge Bayesian methods. It not only underscores the transformative influence of Bayesian approaches but also serves as a beacon, guiding researchers in the judicious selection and refinement of suitable methods for various challenges in structural dynamics. Comments: 144 pages, 9 figures Subjects: Data Analysis, Statistics and Probability (physics.data-an) ; Computational Physics (physics.comp-ph) Cite as: arXiv:2505.22223 [physics.data-an] (or arXiv:2505.22223v2 [physics.data-an] for this version) https://doi.org/10.48550/arXiv.2505.22223 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Yan W J, Mei L F, Yin Y W, et al. Bayesian learning in structural dynamics: a comprehensive review and emerging trends[J]. Mechanical Systems and Signal Processing, 2026, 259: 114661 Related DOI : https://doi.org/10.1016/j.ymssp.2026.114661 Focus to learn more DOI(s) linking to related resources Submission history From: Linfeng Mei [ view email ] [v1] Wed, 28 May 2025 10:54:41 UTC (2,678 KB) [v2] Sun, 1 Jun 2025 13:00:40 UTC (2,936 KB) Full-text links: Access Paper: View PDF view license Current browse context: physics.data-an < prev | next > new | recent | 2025-05 Change to browse by: physics physics.comp-ph References & Citations INSPIRE HEP SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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