  In this paper we introduce Smooth Particle Networks (SPNets), a framework for
integrating fluid dynamics with deep networks. SPNets adds two new layers to
the neural network toolbox: ConvSP and ConvSDF, which enable computing physical
interactions with unordered particle sets. We use these lay- ers in combination
with standard neural network layers to directly implement fluid dynamics inside
a deep network, where the parameters of the network are the fluid parameters
themselves (e.g., viscosity, cohesion, etc.). Because SPNets are imple- mented
as a neural network, the resulting fluid dynamics are fully differentiable. We
then show how this can be successfully used to learn fluid parameters from
data, perform liquid control tasks, and learn policies to manipulate liquids.
