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This paper proposes WaveGraphNet, a coupled inverse--forward graph learning framework for guided-wave damage localization in Carbon Fiber Reinforced Polymer (CFRP) plates. The sensing layout is explicitly modeled as a graph, where transducers are represented as nodes and measured propagation paths define the graph connectivity. An inverse branch maps graph-structured spectral descriptors of differential guided-wave responses to a damage location, while a forward branch predicts the path-wise energy-deviation patterns of measured wave responses associated with a candidate location. During training, the forward branch serves as a physics-consistent regularizer, discouraging location estimates that are numerically plausible but inconsistent with the measured redistribution of wave-response energy. This coupling encourages agreement between inferred damage coordinates and the underlying wave propagation behavior.
Within this benchmark, the proposed graph-based formulation provides a strong localization model for sparse guided-wave sensing and demonstrates improved robustness in extrapolation to held-out regions compared to both non-graph and graph baselines. These results highlight the potential of coupled inverse-forward graph learning as an effective strategy for guided-wave localization under limited spatial coverage.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.20311 [cs.LG] |
| (or arXiv:2605.20311v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20311 arXiv-issued DOI via DataCite |
From: Vinay Sharma [view email]
[v1]
Tue, 19 May 2026 16:51:31 UTC (4,740 KB)
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