Wiewel, SteffenBecher, MoritzThuerey, NilsAlliez, Pierre and Pellacini, Fabio2019-05-052019-05-0520191467-8659https://doi.org/10.1111/cgf.13620https://diglib.eg.org:443/handle/10.1111/cgf13620We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flow problems, and we propose a novel LSTM-based approach to predict the changes of the pressure field over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. We demonstrate for the first time that dense 3D+time functions of physics system can be predicted within the latent spaces of neural networks, and we arrive at a neural-network based simulation algorithm with significant practical speed-ups. We highlight the capabilities of our method with a series of complex liquid simulations, and with a set of single-phase buoyancy simulations. With a set of trained networks, our method is more than two orders of magnitudes faster than a traditional pressure solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.Computing methodologiesNeural networksPhysical simulation10.1111/cgf.1362071-82