Presentation
Neural Kinematic Bases for Fluids
DescriptionWe 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.
Event Type
Technical Papers
TimeMonday, 15 December 20253:22pm - 3:33pm HKT
LocationMeeting Room S221, Level 2