Physics-informed transfer learning for SHM via feature selection

• URL: http://arxiv.org/abs/2507.19519v1
• Date: Fri, 18 Jul 2025 14:28:52 GMT
• Title: Physics-informed transfer learning for SHM via feature selection
• Authors: J. Poole, P. Gardner, A. J. Hughes, N. Dervilis, R. S. Mills, T. A. Dardeno, K. Worden,
• Abstract summary: Transfer learning (TL) can be used to leverage information across related domains.<n>The selection of domains and features is reliant on domain expertise.<n>The modal assurance criterion (MAC) is used to quantify the correspondence between the modes of healthy structures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data used for training structural health monitoring (SHM) systems are expensive and often impractical to obtain, particularly labelled data. Population-based SHM presents a potential solution to this issue by considering the available data across a population of structures. However, differences between structures will mean the training and testing distributions will differ; thus, conventional machine learning methods cannot be expected to generalise between structures. To address this issue, transfer learning (TL), can be used to leverage information across related domains. An important consideration is that the lack of labels in the target domain limits data-based metrics to quantifying the discrepancy between the marginal distributions. Thus, a prerequisite for the application of typical unsupervised TL methods is to identify suitable source structures (domains), and a set of features, for which the conditional distributions are related to the target structure. Generally, the selection of domains and features is reliant on domain expertise; however, for complex mechanisms, such as the influence of damage on the dynamic response of a structure, this task is not trivial. In this paper, knowledge of physics is leveraged to select more similar features, the modal assurance criterion (MAC) is used to quantify the correspondence between the modes of healthy structures. The MAC is shown to have high correspondence with a supervised metric that measures joint-distribution similarity, which is the primary indicator of whether a classifier will generalise between domains. The MAC is proposed as a measure for selecting a set of features that behave consistently across domains when subjected to damage, i.e. features with invariance in the conditional distributions. This approach is demonstrated on numerical and experimental case studies to verify its effectiveness in various applications.

Related papers

• Minimax optimal adaptive structured transfer learning through semi-parametric domain-varying coefficient model [9.091986429838117]
  We study a multi-source, single-target transfer learning problem under conditional distributional drift.<n>We develop an adaptive transfer learning estimator that selectively borrows strength from informative source domains.
  arXiv  Detail & Related papers  (2026-02-20T03:53:06Z)
• MS-ISSM: Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity [65.85858856481131]
  unstructured and irregular nature of point clouds poses a significant challenge for objective quality assessment (PCQA)<n>We propose the Multi-scale Implicit Structural Similarity Measurement (MS-ISSM)
  arXiv  Detail & Related papers  (2026-01-03T14:58:52Z)
• DIGIC: Domain Generalizable Imitation Learning by Causal Discovery [69.13526582209165]
  Causality has been combined with machine learning to produce robust representations for domain generalization. We make a different attempt by leveraging the demonstration data distribution to discover causal features for a domain generalizable policy. We design a novel framework, called DIGIC, to identify the causal features by finding the direct cause of the expert action from the demonstration data distribution.
  arXiv  Detail & Related papers  (2024-02-29T07:09:01Z)
• Quantifying the value of information transfer in population-based SHM [0.0]
  Population-based structural health monitoring (PBSHM) seeks to address some of the limitations associated with data scarcity. The current paper aims to demonstrate a transfer-strategy decision process for a classification task for a population of simulated structures.
  arXiv  Detail & Related papers  (2023-11-06T13:10:38Z)
• Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios [0.0]
  This study extensively compares conventional machine learning methods and deep learning for condition monitoring tasks using an AutoML toolbox. Experiments reveal consistent high accuracy in random K-fold cross-validation scenarios across all tested models. No clear winner emerges, indicating the presence of domain shift in real-world scenarios.
  arXiv  Detail & Related papers  (2023-08-28T14:57:29Z)
• iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models [48.33685559041322]
  This paper focuses on identifying the causal mechanism shifts in two or more related datasets over the same set of variables. Code implementing the proposed method is open-source and publicly available at https://github.com/kevinsbello/iSCAN.
  arXiv  Detail & Related papers  (2023-06-30T01:48:11Z)
• On the Interventional Kullback-Leibler Divergence [11.57430292133273]
  We introduce the Interventional Kullback-Leibler divergence to quantify both structural and distributional differences between causal models. We propose a sufficient condition on the intervention targets to identify subsets of observed variables on which the models provably agree or disagree.
  arXiv  Detail & Related papers  (2023-02-10T17:03:29Z)
• Conditional Feature Importance for Mixed Data [1.6114012813668934]
  We develop a conditional predictive impact (CPI) framework with knockoff sampling. We show that our proposed workflow controls type I error, achieves high power and is in line with results given by other conditional FI measures. Our findings highlight the necessity of developing statistically adequate, specialized methods for mixed data.
  arXiv  Detail & Related papers  (2022-10-06T16:52:38Z)
• Gated Domain Units for Multi-source Domain Generalization [14.643490853965385]
  Distribution shift (DS) occurs when a dataset at test time differs from the dataset at training time. We introduce a modular neural network layer consisting of Gated Domain Units (GDUs) that learn a representation for each latent elementary distribution. During inference, a weighted ensemble of learning machines can be created by comparing new observations with the representations of each elementary distribution.
  arXiv  Detail & Related papers  (2022-06-24T18:12:38Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization [89.73665256847858]
  We show that out-of-distribution performance is strongly correlated with in-distribution performance for a wide range of models and distribution shifts. Specifically, we demonstrate strong correlations between in-distribution and out-of-distribution performance on variants of CIFAR-10 & ImageNet. We also investigate cases where the correlation is weaker, for instance some synthetic distribution shifts from CIFAR-10-C and the tissue classification dataset Camelyon17-WILDS.
  arXiv  Detail & Related papers  (2021-07-09T19:48:23Z)
• Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering [119.88565565454378]
  Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain. We propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one. Our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings.
  arXiv  Detail & Related papers  (2020-12-08T08:52:00Z)
• Adaptive Risk Minimization: Learning to Adapt to Domain Shift [109.87561509436016]
  A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution. In this work, we consider the problem setting of domain generalization, where the training data are structured into domains and there may be multiple test time shifts. We introduce the framework of adaptive risk minimization (ARM), in which models are directly optimized for effective adaptation to shift by learning to adapt on the training domains.
  arXiv  Detail & Related papers  (2020-07-06T17:59:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.