WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Machine Learning
arXiv:2605.20311 (cs)
[Submitted on 19 May 2026]
Title:WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning
View a PDF of the paper titled WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning, by Vinay Sharma and 2 other authors
View PDF
HTML (experimental)
Abstract:Guided-wave structural health monitoring enables damage localization in composite plates using sparse networks of bonded piezoelectric transducers. However, inferring the spatial location of defects from pitch-catch measurements remains weakly constrained when only a limited set of damage locations is available for training. As a result, models trained to predict defect locations may perform well on seen cases but generalize poorly to unseen regions of the structure.
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
|
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
View a PDF of the paper titled WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning, by Vinay Sharma and 2 other authors
References & Citations
Loading...
Bookmark
Bibliographic Tools
Code, Data, Media
Demos
Related Papers
About arXivLabs
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 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
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender
(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models
May 21
-
GraphDiffMed: Knowledge-Constrained Differential Attention with Pharmacological Graph Priors for Medication Recommendation
May 21
-
TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
May 21
-
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
May 21
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.