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.

Questo articolo esplora i giri e le loro implicazioni.

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