A Novel Empirical Interpolation Surrogate for Digital Twin Wave-Based Structural Health Monitoring with MATLAB Implementation

Abhilash Sreekumar, Linjun Zhong () and Dimitrios Chronopoulos
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Abhilash Sreekumar: Department of Mechanical Engineering & Mecha(tro)nic System Dynamics (LMSD), KU Leuven, 9000 Gent, Belgium
Linjun Zhong: Department of Mechanical Engineering & Mecha(tro)nic System Dynamics (LMSD), KU Leuven, 9000 Gent, Belgium
Dimitrios Chronopoulos: Department of Mechanical Engineering & Mecha(tro)nic System Dynamics (LMSD), KU Leuven, 9000 Gent, Belgium

Mathematics, 2025, vol. 13, issue 11, 1-22

Abstract: Guided-wave structural health monitoring offers exceptional sensitivity to localized defects but relies on high-fidelity simulations that are prohibitively expensive for real-time use. Reduced-order models can alleviate this cost but hinge on affine parameterization of system operators. This assumption breaks down for complex, non-affine damage behavior. To overcome these limitations, we introduce a novel, non-intrusive space–time empirical interpolation method that is applied directly to the full wavefield. By greedily selecting key spatial, temporal, and parametric points, our approach builds an affine-like reduced model without modifying the underlying operators. We then train a Gaussian-process surrogate to map damage parameters straight to interpolation coefficients, enabling true real-time digital-twin predictions. Validation on both analytic and finite-element benchmarks confirms the method’s accuracy and speed-ups. All MATLAB 2024b. scripts for EIM, DEIM, Kriging, and wave propagation are available in the GitHub (version 3.4.20) repository referenced in the Data Availability statement, ensuring full reproducibility.

Keywords: structural health monitoring; digital twins; surrogate models; model order reduction; wave propagation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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