short-fiber-thermoplastics

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.