Adaptive Interaction Graphs for Particle Simulation

This research paper targets particle simulation with graph neural networks, where fixed interaction topologies can contribute to compounded errors during long-horizon rollouts. The authors propose an adaptive interaction graph controlled by per-particle uncertainty estimates, with a variance head trained jointly with an acceleration head using a heteroscedastic Gaussian objective.