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arXiv Machine Learning By Aiden Zhou

Adaptive Interaction Graphs for Particle Simulation

Read the original on arXiv Machine Learning →

The paper introduces AdaptGNS, a particle simulation framework that dynamically adjusts the interaction graph based on per-particle uncertainty estimates. By training a variance head jointly with the acceleration head using a heteroscedastic Gaussian NLL loss, the model expands the neighborhood of high‑uncertainty particles, improving long‑horizon accuracy. Experiments show a strict Pareto improvement on the WaterDrop benchmark and a modest gain on Sand, suggesting adaptive graphs are especially beneficial in spatially complex regions.

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