The video for our latent-space physics paper is finally online! It’s been a while, the first paper version was on online on arXiv in February 2018 🙂 The paper will now be presented at Eurographics 2019 in Genoa.

Abstract: Our work explores methods for the data-driven inference of temporal evolutions of physical functions with deep learning techniques. More specifically, we target Navier-Stokes / fluid flow problems, and we propose a novel network architecture 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 based on convolutional neural networks, as well as a special architecture for temporal prediction. We demonstrate that dense 3D+time functions of physics system can be predicted with neural networks, and we arrive at a neural-network based simulation algorithm with practical speed-ups. We demonstrate the capabilities of our method with a series of complex liquid simulations, and with a set of single-phase simulations. Our method predicts pressure fields very efficiently. It is more than two orders of magnitudes faster than a regular solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.

More detailed infos can be found here