Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics


We present a novel method for guaranteeing linear momentum in learned physics simulations. Unlike existing methods, we enforce conservation of momentum with a hard constraint, which we realize via antisymmetrical continuous convolutional layers. We combine these strict constraints with a hierarchical network architecture, a carefully constructed resampling scheme, and a training approach for temporal coherence. In combination, the proposed method allows us to substantially increase the physical accuracy of the learned simulator. In addition, the induced physics bias leads to significantly better generalization performance and makes our method more reliable in unseen test cases. We evaluate our method on a range of different, challenging fluid scenarios, and show that the proposed algorithm can learn complex dynamics while outperforming existing approaches in terms of generalization and training performance.