Explore missing flow dynamics by physics-informed deep learning: The parameterized governing systems-Reference-Cited by-同舟云学术

Explore missing flow dynamics by physics-informed deep learning: The parameterized governing systems

Published:2021-09 Issue:9 Volume:33 Page:095116
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Xu Hui¹^ORCID,Zhang Wei²^ORCID,Wang Yong³^ORCID

Affiliation:

1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China

2. Science and Technology on Water Jet Propulsion Laboratory, Marine and Research Institute of China, Shanghai 200011, China

3. Laboratory for Fluid Physics, Pattern Formation and Biocomplexity, Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany

Funder

National Natural Science Foundation of China

National Numerical Wind Tunnel Project of China

Publisher

AIP Publishing

Subject

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

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0062377

Reference28 articles.

1. Machine Learning for Fluid Mechanics

2. Turbulence Modeling in the Age of Data

3. Some Recent Developments in Turbulence Closure Modeling

4. Data-Free and Data-Driven RANS Predictions with Quantified Uncertainty

5. Data assimilation and resolvent analysis of turbulent flow behind a wall-proximity rib

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