Unsupervised deep learning for super-resolution reconstruction of turbulence

Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows have used supervised learning, which requires paired data for training. This limitation hinders more practical applications of super-resolution reconstruction. Therefore, we present an unsupervised learning m...

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Bibliographic Details
Published in:	Journal of fluid mechanics Vol. 910
Main Authors:	Kim, Hyojin, Kim, Junhyuk, Won, Sungjin, Lee, Changhoon
Format:	Journal Article
Language:	English
Published:	Cambridge, UK Cambridge University Press 13.01.2021
Subjects:	Channel flow Computational fluid dynamics Data Deep learning Direct numerical simulation Fields Fluid dynamics Fluid flow Isotropic turbulence JFM Papers Large eddy simulation Mathematical models Neural networks Noise Oceanic eddies Ordinary differential equations Reconstruction Resolution Reynolds number Simulation Training Turbulence turbulence simulation
ISSN:	0022-1120, 1469-7645
Online Access:	Get full text
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