A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling
This paper proposes a hybrid GNN-FEM framework that integrates a graph neural network surrogate for phase-field updates within a conventional finite element staggered scheme, enabling efficient, accurate, and generalizable simulations of history-dependent fracture evolution across diverse geometries and conditions while preserving physical consistency.