This study introduces a two-scale Graph Neural Operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures. LGN has two networks: LGN-i, learning the reduced dynamics of lattices, and LGN-ii, learning the mapping from the reduced representation onto the tetrahedral mesh. LGN can predict deformation for arbitrary lattices, therefore the name operator. Our approach significantly reduces inference time while maintaining high accuracy for unseen simulations, establishing the use of GNOs as efficient surrogate models for evaluating mechanical responses of lattices and structures.

翻译：本研究提出了一种两尺度图神经算子（GNO），即LatticeGraphNet（LGN），作为三维点阵零件及结构的高成本非线性有限元仿真的替代模型。LGN包含两个网络：LGN-i用于学习点阵的降阶动力学，LGN-ii则学习从降阶表征到四面体网格的映射。由于LGN能够预测任意点阵的形变，故称之为算子。该方法在保持对未见仿真高准确度的同时，显著缩短了推理时间，确立了GNO作为评估点阵与结构力学响应的高效替代模型的应用价值。