Learning to Simulate Complex Physical Systems: A Case Study

Complex physical system simulation is important in many real world applications. We study the general simulation scenario to generate the response result when a physical object is applied by external factors. Traditional solvers on Partial Differential Equations (PDEs) suffer from significantly high computational cost. Many recent learning-based approaches focus on multivariate time series alike simulation prediction problem and do not work for our case. In this paper, we propose a novel two-level graph neural networks (GNNs) to learn the simulation result of a physical object applied by external factors. The key is a two-level graph structure where one fine mesh graph is mapped to multiple coarse one. Our preliminary evaluation on both synthetic and real datasets demonstrates that our work outperforms three state-of-the-arts by much lower errors.