Digital Library Deep Learning and Physical Models Wiewel, S., Becher, M., Thuerey, N.: Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow, arXiv, submitted to NIPS 2018, 2018 Abstract: Our work explores methods for the data-driven inference of temporal evolutions of physical functions with deep learning techniques. 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. Key for arriving at a feasible algorithm is a technique for dimensionality reduction...