Forecasting and Interpolation for Learning Physical Simulation over Meshes
Speaker: Xiao Luo, Ph.D.
IDRE Fellow
Department of Computer Science
University of California Los Angeles
Abstract: This talk discusses the problem of learning-based physical simulation, a crucial task with applications in fluid mechanics and aerodynamics. Recent works typically utilize graph neural networks (GNNs) to produce next-time states on irregular meshes by modeling interacting dynamics and then adopting iterative rollouts for the whole trajectories. Our work proposes a simple yet effective approach named FAIR for long-term mesh-based simulations. Our model employs a continuous graph ODE model that incorporates past states into the evolution of interacting node representations, capable of learning coarse long-term trajectories under a multi-task learning framework. Then, we leverage a channel aggregation strategy to summarize the trajectories for refined short-term predictions, which can be illustrated using an interpolation process. Our method can generate accurate long-term trajectories through pyramid-like alternative propagation between the foresight step and refinement step. Finally, we show the experiments on several benchmark datasets to validate the effectiveness of our method.
Watch video Forecasting and Interpolation for Learning Physical Simulation over Meshes online without registration, duration hours minute second in high quality. This video was added by user UCLA Office of Advanced Research Computing (OARC) 02 September 2024, don't forget to share it with your friends and acquaintances, it has been viewed on our site 88 once and liked it 1 people.