Liquids exhibit complex non-linear behavior under changing simulation conditions such as user interactions.
We proposed 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 leveraged the power of generative neural networks to learn a reduced representation.
We introduced 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 showcased 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.
[Lukas Prantl, Boris Bonev and Nils Thuerey]