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Fluid Simulation on Neural Flow Maps
We introduce Neural Flow Maps, a novel simulation method bridging the emerging paradigm of implicit neural representations with fluid simulation based on the theory of flow maps, to achieve state-of-the-art simulation of inviscid fluid phenomena.
Inferring Hybrid Neural Fluid Fields from Videos
Specifically, to deal with visual ambiguities of fluid velocity, we introduce a set of physics-based losses that enforce inferring a physically plausible velocity field, which is divergence-free and drives the transport of density.
Learning Vortex Dynamics for Fluid Inference and Prediction
We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video.
VortexNet: Learning Complex Dynamic Systems with Physics-Embedded Networks
Since the number of such vortices are much smaller than that of the Eulerian, grid discretization, this Lagrangian discretization in essence encodes the system dynamics on a compact physics-based latent space.
Neural Vortex Method: from Finite Lagrangian Particles to Infinite Dimensional Eulerian Dynamics
To tackle this challenge, we propose a novel learning-based framework, the Neural Vortex Method (NVM), which builds a neural-network description of the Lagrangian vortex structures and their interaction dynamics to reconstruct the high-resolution Eulerian flow field in a physically-precise manner.
