Learning to Simulate Complex Physics with Graph Networks
Learning to Simulate Complex Physics with Graph NetworksAlvaro Sanchez-Gonzalez* 1Jonathan Godwin* 1Tobias Pfaff* 1Rex Ying* 1 2Jure Leskovec2Peter W. Battaglia1AbstractHere we present a machine Learning frameworkand model implementation that can learn tosimulate a wide variety of challenging physi-cal domains, involving fluids, rigid solids, anddeformable materials interacting with one an-other. Our framework which we term GraphNetwork-based Simulators (GNS) representsthe state of a physical system with particles, ex-pressed as nodes in a Graph , and computes dy-namics via learned message-passing. Our re-sults show that our model can generalize fromsingle-timestep predictions with thousands of par-ticles during training, to different initial condi-tions, thousands of timesteps, and at least anorder of magnitude more particles at test model was robust to hyperparameter choicesacross various evaluation metrics: the main de-terminants of long-term performance were thenumber of message-passing steps, and mitigat-ing the accumulation of error by corrupting thetraining data with noise.
Learning to Simulate Complex Physics with Graph Networks Alvaro Sanchez-Gonzalez * 1Jonathan Godwin Tobias Pfaff Rex Ying* 1 2 Jure Leskovec2 Peter W. Battaglia1 Abstract Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physi-cal domains, involving fluids, rigid ...
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