CORGI: GNNs with Convolutional Residual Global Interactions for Lagrangian Simulation

TL;DR

CORGI enhances GNN-based fluid simulation models by integrating a lightweight global interaction module, significantly improving accuracy with minimal additional computational cost.

Contribution

Introduces CORGI, a hybrid GNN architecture with a global context module that improves fluid simulation accuracy efficiently.

Findings

57% improvement in rollout accuracy over GNS

49% accuracy increase compared to SEGNN

CORGI reduces inference and training time while boosting accuracy

Abstract

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.