SPNets: Differentiable Fluid Dynamics for Deep Neural Networks

Connor Schenck, Dieter Fox

Abstract:

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 layers 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 implemented 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.