geometric deep learning (GDL) models have demonstrated a great potential for
the analysis of non-Euclidian data. They are developed to incorporate the
geometric and topological information of non-Euclidian data into the end-to-end
deep learning architectures. Motivated by the recent su
通过创造物理系统的 3D 多体点云,我们提出了一种新型的基于等变矩阵乘积态 (MPS) 的消息传递策略,有效地建模复杂的多体关系并捕捉了几何图中的对称性,超越了现有的几何图神经网络的平均场近似,并在预测经典牛顿系统和量子张量哈密顿矩阵等基准任务上验证了其卓越的准确性,堪称参数化几何张量网络的创新应用。