Deep learning methods for super-resolution reconstruction of turbulent flows-Reference-Cited by-同舟云学术

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Deep learning methods for super-resolution reconstruction of turbulent flows

Published:2020-02-01 Issue:2 Volume:32 Page:025105
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Liu Bo¹,Tang Jiupeng¹,Huang Haibo¹^ORCID,Lu Xi-Yun¹

Affiliation:

1. Department of Modern Mechanics, University of Science and Technology of China, Hefei 230026, China

Funder

Foundation for Innovative Research Groups of the National Natural Science Foundation of China

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5140772

Reference37 articles.

1. Twenty years of particle image velocimetry

2. Closed-Loop Turbulence Control: Progress and Challenges

3. Deep learning in fluid dynamics

4. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

5. Data-driven prediction of unsteady flow over a circular cylinder using deep learning

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