Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow

Steffen Wiewel; Moritz Becher; Nils Thuerey

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arXiv · 1802.10123

Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow

Steffen Wiewel·Moritz Becher·Nils Thuerey

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Abstract

We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flows, i.e. Navier-Stokes problems, and we propose a novel LSTM-based approach to predict the changes of pressure fields 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.

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BibTeX RIS

Steffen Wiewel, Moritz Becher, Nils Thuerey. 2019-03-05. Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow. https://arxiv.org/abs/1802.10123

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