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Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow
Author(s) -
Wiewel S.,
Becher M.,
Thuerey N.
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13620
Subject(s) - computer science , curse of dimensionality , inference , eulerian path , solver , artificial neural network , context (archaeology) , set (abstract data type) , flow (mathematics) , deep learning , series (stratigraphy) , artificial intelligence , fluid dynamics , algorithm , physics , mathematics , mechanics , paleontology , lagrangian , biology , programming language
We 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.