Graph neural networks for simulating crack coalescence and propagation in brittle materials

Understand

High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics-based models quickly become computationally expensive as the number of microcracks increases.

• This work develops a Graph Neural Network (GNN) based framework to simulate fracture and stress evolution in brittle materials due to multiple microcracks' interaction.
• The GNN framework is trained on the dataset generated by XFEM-based fracture simulator.
• Our framework achieves high prediction accuracy on the test set (compared to an XFEM-based fracture simulator) by engineering a sequence of GNN-based predictions.