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.

由于裂纹形状的快速变化，裂纹模拟的网格生成通常是限速步骤。经典的网格划分范例，即放置节点和链接，依赖于预定义的规则，并且无法概括各种裂纹形状。我们提出了一种基于图神经网络的方法来恢复裂纹网格中丢失的连接信息。约束 Delaunay 三角剖分方法创建了具有不同裂纹形状的代表性训练网格数据集。全面系统的分析比较了不同图形表示下五种最先进的 GNN 的有效性和效率。我们的研究表明，经过训练的 GraphSAGE 在三角网格划分任务上的表现优于最先进的 GNN，并揭示了 GNN 恢复邻接顶点或边之间缺失信息的潜力。这项工作开创了 GNN 在智能网格生成中的应用，并为复杂裂纹模拟铺平了道路。