Our work on “Generating Liquid Simulations with Deformation-aware Neural Networks” has been conditionally accepted at the International Conference on Learning Representations (ICLR), and will be presented there in May.

It focuses on an approach to pre-compute solution spaces for free-surface Navier-Stokes with deformation learning. The first version of our work appeared in April 2017, so video, paper and the accompanying demo Android app can all be already found online. More information here.

Liquids exhibit complex non-linear behavior under changing simulation conditions such as user interactions. We propose a method to map this complex behavior over a parameter range onto reduced representation based on space-time deformations. In order to represent the complexity of the full space of inputs, we leverage the power of generative neural networks to learn a reduced representation. We introduce a novel deformation-aware loss function, which enables optimization in the highly non-linear space of multiple deformations. To demonstrate the effectiveness of our approach, we showcase the method with several complex examples in two and four dimensions. Our representation makes it possible to generate implicit surfaces of liquids very efficiently, which makes it possible to display the scene from any angle, and to add secondary effects such as particle systems. We have implemented a mobile application for our full output pipeline to demonstrate that real-time interaction is possible with our approach.