Our paper “Deep Fluids: A Generative Network for Parameterized Fluid Simulations” in collaboration with the CGL of ETH Zurich is online now!

You can check it out here:

The goal is a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. We also demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network.