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

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 repository referenced in the Data Availability statement, ensuring full reproducibility.