Spatio-temporal deep learning models of 3D turbulence with physics informed diagnostics-Reference-Cited by-同舟云学术

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Spatio-temporal deep learning models of 3D turbulence with physics informed diagnostics

Published:2020-10-02 Issue:9-10 Volume:21 Page:484-524
ISSN:1468-5248
Container-title:Journal of Turbulence
language:en
Short-container-title:Journal of Turbulence

Author:

Mohan Arvind T.¹²^ORCID,Tretiak Dima³²,Chertkov Misha⁴,Livescu Daniel²

Affiliation:

1. Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA

2. Computational Physics and Methods Group, Los Alamos National Laboratory, Los Alamos, NM, USA

3. Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

4. Program in Applied Mathematics, University of Arizona, Tucson, AZ, USA

Funder

U.S. Department of Energy

LDRD

Los Alamos National Laboratory

Publisher

Informa UK Limited

Subject

General Physics and Astronomy,Mechanics of Materials,Condensed Matter Physics,Computational Mechanics

Link

https://www.tandfonline.com/doi/pdf/10.1080/14685248.2020.1832230

Reference75 articles.

1. Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

2. Wang JX, Wu J, Ling J, et al. A comprehensive physics-informed machine learning framework for predictive turbulence modeling. Preprint arXiv:170107102. 2017.

3. Tracey BD, Duraisamy K, Alonso JJ. A machine learning strategy to assist turbulence model development. In: Proceedings of the 53rd AIAA Aerospace Sciences Meeting; Kissimmee, Florida, Publisher: AIAA (American Institute of Aeronautics and Astronautics), 2015. p. 1287.

4. Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils

5. Subgrid modelling for two-dimensional turbulence using neural networks

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