用轻量级全局交互模块提升图神经网络对流体模拟的精度
CORGI: GNNs with Convolutional Residual Global Interactions for Lagrangian Simulation
- 在GNN基础上加卷积网格模块，捕捉流体长程相互作用
- 相比GNS，滚动预测准确率提升57%，推理时间仅多13%
- 适合需要高精度且算力受限的物理模拟场景
偏微分方程是动力系统建模的核心，尤其在流体动力学中，传统求解器常因非线性和计算成本而受限。基于粒子的拉格朗日神经代理模型（如GNS和SEGNN）已成为有效替代方案，但通常受制于有限的感受野，难以捕捉流体流动中的全局相互作用。为此，我们提出卷积残差全局交互（CORGI），一种混合架构，在任意GNN求解器上引入轻量级欧拉组件以聚合全局上下文。通过将粒子特征投影到网格、进行卷积更新并映射回粒子域，CORGI在几乎无额外开销下捕获长程依赖。应用于GNS主干时，其滚动预测准确率提升57%，推理时间仅增加13%，训练时间增加31%；相比SEGNN，准确率提升49%，推理时间减少48%，训练时间减少30%。即使在相同运行时约束下，CORGI平均比GNS高出47%，展现出在不同算力预算下的优异性能与通用性。
原文摘要 · Abstract (English)
Partial differential equations (PDEs) are central to dynamical systems modeling, particularly in hydrodynamics, where traditional solvers often struggle with nonlinearity and computational cost. Lagrangian neural surrogates such as GNS and SEGNN have emerged as strong alternatives by learning from particle-based simulations. However, these models typically operate with limited receptive fields, making them inaccurate for capturing the inherently global interactions in fluid flows. Motivated by this observation, we introduce Convolutional Residual Global Interactions (CORGI), a hybrid architecture that augments any GNN-based solver with a lightweight Eulerian component for global context aggregation. By projecting particle features onto a grid, applying convolutional updates, and mapping them back to the particle domain, CORGI captures long-range dependencies without significant overhead. When applied to a GNS backbone, CORGI achieves a 57% improvement in rollout accuracy with only 13% more inference time and 31% more training time. Compared to SEGNN, CORGI improves accuracy by 49% while reducing inference time by 48% and training time by 30%. Even under identical runtime constraints, CORGI outperforms GNS by 47% on average, highlighting its versatility and performance on varied compute budgets.
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