Chentanez, NuttapongJeschke, StefanMüller, MatthiasMacklin, MilesDominik L. MichelsSoeren Pirk2022-08-102022-08-1020221467-8659https://doi.org/10.1111/cgf.14643https://diglib.eg.org:443/handle/10.1111/cgf14643We propose a hierarchical graph for learning physics and a novel way to handle obstacles. The finest level of the graph consist of the particles itself. Coarser levels consist of the cells of sparse grids with successively doubling cell sizes covering the volume occupied by the particles. The hierarchical structure allows for the information to propagate at great distance in a single message passing iteration. The novel obstacle handling allows the simulation to be obstacle aware without the need for ghost particles. We train the network to predict effective acceleration produced by multiple sub-steps of 3D multi-material material point method (MPM) simulation consisting of water, sand and snow with complex obstacles. Our network produces lower error, trains up to 7.0X faster and inferences up to 11.3X faster than [SGGP*20]. It is also, on average, about 3.7X faster compared to Taichi Elements simulation running on the same hardware in our tests.CCS Concepts: Computing methodologies --> Neural networks; Physical simulationComputing methodologiesNeural networksPhysical simulation10.1111/cgf.14643283-29210 pages