We’re happy to report that our paper “Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics” has been accepted at NeurIPS and is available now at https://arxiv.org/abs/2210.06036.
Here’s a preview of a generalization test where we apply our model (without changes, i.e. same time step as before) to a scene with over 1 million particles:
Interestingly, the convolutional architecture leads to lean & efficient models that outperform architectures such as graph-nets, the positional inductive bias turns out to be important for physics simulations
Source code and data sets are already available under “Deep Momentum Conserving Fluids” on github: https://github.com/tum-pbs/DMCF
Full paper abstract: 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 increase the physical accuracy of the learned simulator substantially. In addition, the induced physical 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. Among others, we demonstrate that our approach generalizes to new scenarios with up to one million particles. Our results show that the proposed algorithm can learn complex dynamics while outperforming existing approaches in generalization and training performance.
