Intelligent mesh generation for crack simulation using graph neural networks

Mesh generation for crack simulation is often the rate-limiting step because of the rapid variations in crack shape. The classical meshing paradigm, place-nodes-and-link, relies on predefined rules and fails to generalize various crack shapes. We proposed a graph neural networks-based method for recovering the missing connection information in the crack meshes. The constrained Delaunay triangulation method created a representative training mesh dataset with different crack shapes. Comprehensive and systematic analyses compare the effectiveness and efficiency of five state-of-art GNNs under different graph representations. Our study shows that the trained GraphSAGE outperforms the state-of-art GNNs on the triangular meshing task efficiently and reveals GNNs' potential to restore the missing information between adjacency vertices or edges. This work pioneers the application of GNNs for intelligent mesh generation and paves the way for complex crack simulation.

Automated summary

This study proposes a graph neural networks-based method to recover the missing connection information in crack meshes, and comparative analysis shows that the trained GraphSAGE outperforms other GNNs on triangular meshing task, revealing the potential of GNNs in restoring missing information.