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This paper proposes a data-driven surrogate modeling framework using a hybrid Graph Neural Network-Long Short-Term Memory architecture to predict the static response of additively manufactured short-fiber thermoplastics, achieving high accuracy (R²≈0.98) and two orders of magnitude speedup over finite element simulations.
WaveGraphNet is a coupled inverse-forward graph learning framework for guided-wave damage localization in composite plates, using sparse transducer networks and graph-based spectral descriptors to improve spatial generalization under limited training data.