论文标题
保证为学习粒子流体动力学的动量保存
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics
论文作者
论文摘要
我们提出了一种新的方法，可以保证在学习的物理模拟中线性动量。与现有的方法不同，我们通过硬约束来强制对动量保护，我们通过反对称的连续卷积层实现。我们将这些严格的约束与分层网络架构，经过精心构造的重采样方案以及时间连贯性的培训方法相结合。结合起来，提出的方法使我们能够大大提高学习模拟器的物理准确性。此外，诱导的身体偏置会导致明显更好的泛化性能，并使我们的方法在看不见的测试案例中更可靠。我们在各种不同，具有挑战性的流体情况下评估我们的方法。除其他外，我们证明我们的方法概括为新场景，最多有100万个粒子。我们的结果表明，所提出的算法可以学习复杂的动态，同时胜过概括和培训性能的现有方法。我们的方法的实现可在https://github.com/tum-pbs/dmcf上获得。
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. An implementation of our approach is available at https://github.com/tum-pbs/DMCF.
