We propose a novel approach for deformation-aware neural networks that learn the weighting and synthesis of dense volumetric deformation fields. Our method specifically targets the space-time representation of physical surfaces from liquid simulations. Liquids exhibit highly complex, non-linear behavior under changing simulation conditions such as different initial conditions. Our algorithm captures these complex phenomena in two stages: a first neural network computes a weighting function for a set of pre-computed deformations, while a second network directly generates a deformation field for refining the surface. Key for successful training runs in this setting is a suitable loss function that encodes the effect of the deformations, and a robust calculation of the corresponding gradients. To demonstrate the effectiveness of our approach, we showcase our method with several complex examples of flowing liquids with topology changes. Our representation makes it possible to rapidly generate the desired implicit surfaces. We have implemented a mobile application to demonstrate that real-time interactions with complex liquid effects are possible with our approach.
Learning weighting and deformations of space-time data sets for highly efficient approximations of liquid behavior. A neural-network based model is used to interpolate simulations for novel scene conditions from densely registered 4D implicit surfaces for a structured scene. This paper presents a coupled deep learning approach for generating realistic liquid simulation data that can be useful for real-time decision support applications. This paper introduces a deep learning approach for physical simulation that combines two networks for synthesizing 4D data that represents 3D physical simulations