Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows

We present a new data reconstruction method with supervised machine learning techniques inspired by super resolution and inbetweening to recover high-resolution turbulent flows from grossly coarse flow data in space and time. For the present machine-learning-based data reconstruction, we use the dow...

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Bibliographic Details
Published in:	Journal of fluid mechanics Vol. 909
Main Authors:	Fukami, Kai, Fukagata, Koji, Taira, Kunihiko
Format:	Journal Article
Language:	English
Published:	Cambridge, UK Cambridge University Press 25.02.2021
Subjects:	Artificial neural networks Channel flow Computational fluid dynamics Computer applications Correlation coefficient Correlation coefficients Cylinders Data Direct numerical simulation Evolution Fluid dynamics Fluid flow Isotropic turbulence JFM Papers Learning algorithms Machine learning Mathematical models Neural networks Reconstruction Resolution Robustness (mathematics) Three dimensional flow Three dimensional models Training Turbulence Turbulent flow Two dimensional models computational methods turbulence simulation
ISSN:	0022-1120, 1469-7645
Online Access:	Get full text
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