Modeling collective dynamics in multi-agent systems is challenging due to the complexity of short and long-range interactions. Here, the authors develop hierarchical and equivariant graph neural networks that accurately predict local and global behaviors, outperforming traditional GNNs in forecasting collective motion in vortex clusters and microswimmers.
- Alec J. Linot
- Haotian Hang
- Kunihiko Taira
