Neural Kinematic Bases for Fluids

We propose mesh-free fluid simulations that exploit a kinematic neural basis for velocity fields represented by an MLP. We design a set of losses that ensures that these neural bases satisfy fundamental physical properties such as orthogonality, divergence-free, boundary alignment, and smoothness. Our neural bases can then be used to fit an input sketch of a flow, which will inherit the same fundamental properties from the bases. We then can animate such flow in real-time using standard time integrators. Our neural bases can accommodate different domains and naturally extend to three dimensions.

@article{liu2025_2504.15657,
  title={ Neural Kinematic Bases for Fluids },
  author={ Yibo Liu and Paul Kry and Kenny Erleben and Noam Aigerman and Sune Darkner and Teseo Schneider },
  journal={arXiv preprint arXiv:2504.15657},
  year={ 2025 }
}