动态构建层次图结构，让物理仿真更自适应高效
EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
- 用可微分方法自动学习层次图结构与物理演化
- 在5个基准数据集上显著优于固定层级模型
- 适合需要高精度物理仿真的研究者与工程师
图神经网络在基于网格的物理仿真中表现强大。为高效建模大规模系统，现有方法通常采用人工设计且固定的层次图结构来捕捉多尺度节点关系。然而，这种固定结构难以适应复杂物理系统的动态变化。本文提出EvoMesh，一种完全可微的框架，能够联合学习图层次结构与物理动态，由物理输入自适应引导。EvoMesh引入各向异性消息传递，在每一层内实现方向性特征聚合，同时根据物理上下文学习节点选择概率以决定下一层节点。该设计形成更灵活的消息捷径，增强对长距离依赖的捕捉能力。在五个基准物理仿真数据集上的大量实验表明，EvoMesh显著优于近期固定层级消息传递网络。项目页面见：https://hbell99.github.io/evo-mesh/
原文摘要 · Abstract (English)
Graph neural networks have been a powerful tool for mesh-based physical simulation. To efficiently model large-scale systems, existing methods mainly employ hierarchical graph structures to capture multi-scale node relations. However, these graph hierarchies are typically manually designed and fixed, limiting their ability to adapt to the evolving dynamics of complex physical systems. We propose EvoMesh, a fully differentiable framework that jointly learns graph hierarchies and physical dynamics, adaptively guided by physical inputs. EvoMesh introduces anisotropic message passing, which enables direction-specific aggregation of dynamic features between nodes within each hierarchy, while simultaneously learning node selection probabilities for the next hierarchical level based on physical context. This design creates more flexible message shortcuts and enhances the model's capacity to capture long-range dependencies. Extensive experiments on five benchmark physical simulation datasets show that EvoMesh outperforms recent fixed-hierarchy message passing networks by large margins. The project page is available at https://hbell99.github.io/evo-mesh/.
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