# Granular Flow Prediction

Particle-resolved granular flow simulation has been limited by repeated information passing over the full particle graph. HCG-GNN moves 5 of its 7 message-passing steps onto two levels of coarse graphs built from occupied cells, matching the multi-step accuracy of a conventional particle-level GNN in inclined-plane granular flow benchmarks while training 6.47 times faster with 46.2% less GPU memory; a model trained on up to 5000 grains predicted a 2 s release of 10000 grains in 31 s, versus 2000 s for DEM under the same benchmark conditions.

Particle-resolved granular flow simulation has been limited by repeated information passing over the full particle graph. HCG-GNN moves 5 of its 7 message-passing steps onto two levels of coarse graphs built from occupied cells, matching the multi-step accuracy of a conventional particle-level GNN in inclined-plane granular flow benchmarks while training 6.47 times faster with 46.2% less GPU memory; a model trained on up to 5000 grains predicted a 2 s release of 10000 grains in 31 s, versus 2000 s for DEM under the same benchmark conditions.