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arXiv · 2504.15657

Neural Kinematic Bases for Fluids

Yibo Liu·Zhixin Fang·Sune Darkner·Noam Aigerman·Kenny Erleben·Paul Kry·Teseo Schneider
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Abstract

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 approximate 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, moving boundaries, and naturally extend to three dimensions.

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BibTeXRIS

Yibo Liu, Zhixin Fang, Sune Darkner, Noam Aigerman, Kenny Erleben, Paul Kry, Teseo Schneider. 2025-04-22. Neural Kinematic Bases for Fluids. https://doi.org/10.1145/3757377.3763925

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