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Journal of Turbulence Volume 21, 2020 - Issue 9-10: The machine learning: Guest edited by Federico Toschi
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Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow

Rui Fanga Institute for Applied Computational Science, Harvard University, Cambridge, MA, USA;b The University of Texas at Austin, Austin, TX, USACorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-6586-502X
ORCID Icon,
David Sondaka Institute for Applied Computational Science, Harvard University, Cambridge, MA, USA
,
Pavlos Protopapasa Institute for Applied Computational Science, Harvard University, Cambridge, MA, USA
&
Sauro Succia Institute for Applied Computational Science, Harvard University, Cambridge, MA, USA;c Center for Life Nanoscience at la Sapienza, Istituto Italiano di Tecnologia, Rome, Italy;d Istituto Applicazioni del Calcolo, National Research Council of Itaty, Rome, Italy
Pages 525-543 | Received 04 Jul 2019, Accepted 09 Dec 2019, Published online: 24 Dec 2019

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Arsen S. Iskhakov, Cheng-Kai Tai, Igor A. Bolotnov & Nam T. Dinh. (2023) A Perspective on Data-Driven Coarse Grid Modeling for System-Level Thermal Hydraulics. Nuclear Science and Engineering 197:10, pages 2527-2542.
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Sandeep Pandey, Jörg Schumacher & Katepalli R. Sreenivasan. (2020) A perspective on machine learning in turbulent flows. Journal of Turbulence 21:9-10, pages 567-584.
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Articles from other publishers (19)

Pushan Sharma, Wai Tong Chung, Bassem Akoush & Matthias Ihme. (2023) A Review of Physics-Informed Machine Learning in Fluid Mechanics. Energies 16:5, pages 2343.
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Fujihiro Hamba. (2022) Analysis and modelling of non-local eddy diffusivity for turbulent scalar flux. Journal of Fluid Mechanics 950.
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J. Williams, U. Wolfram & A. Ozel. (2022) Neural stochastic differential equations for particle dispersion in large-eddy simulations of homogeneous isotropic turbulence. Physics of Fluids 34:11.
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S. Berrone & D. Oberto. (2022) An invariances-preserving vector basis neural network for the closure of Reynolds-averaged Navier–Stokes equations by the divergence of the Reynolds stress tensor. Physics of Fluids 34:9.
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Li-Tao Zhu, Xi-Zhong ChenBo Ouyang, Wei-Cheng Yan, He Lei, Zhe Chen & Zheng-Hong Luo. (2022) Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors. Industrial & Engineering Chemistry Research 61:28, pages 9901-9949.
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A. Biswas, J. Tian & S. Ulusoy. (2022) Error estimates for deep learning methods in fluid dynamics. Numerische Mathematik 151:3, pages 753-777.
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Marios Mattheakis, David Sondak, Akshunna S. Dogra & Pavlos Protopapas. (2022) Hamiltonian neural networks for solving equations of motion. Physical Review E 105:6.
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Jyoti P Panda & Hari V Warrior. (2021) Modeling the pressure strain correlation in turbulent flows using deep neural networks. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236:7, pages 3447-3458.
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A. Golsa Tabe Jamaat & B. Yuji Hattori. (2022) Development of subgrid-scale model for LES of Burgers turbulence with large filter size. Physics of Fluids 34:4.
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Jyoti Prakash Panda & Hari Vijayan Warrior. (2022) Evaluation of machine learning algorithms for predictive Reynolds stress transport modeling预测雷诺应力输运模型的机器学习算法评估. Acta Mechanica Sinica 38:4.
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R. McConkey, E. Yee & F. S. Lien. (2022) Deep structured neural networks for turbulence closure modeling. Physics of Fluids 34:3.
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Salar Taghizadeh, Freddie D. Witherden, Yassin A. Hassan & Sharath S. Girimaji. (2021) Turbulence closure modeling with data-driven techniques: Investigation of generalizable deep neural networks. Physics of Fluids 33:11.
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Pedro M. Milani, Julia Ling & John K. Eaton. (2021) On the generality of tensor basis neural networks for turbulent scalar flux modeling. International Communications in Heat and Mass Transfer 128, pages 105626.
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Ryley McConkey, Eugene Yee & Fue-Sang Lien. (2021) A curated dataset for data-driven turbulence modelling. Scientific Data 8:1.
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Chenyue Xie, Xiangming Xiong & Jianchun Wang. (2021) Artificial neural network approach for turbulence models: A local framework. Physical Review Fluids 6:8.
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Shanti Bhushan, Greg W. Burgreen, Wesley Brewer & Ian D. Dettwiller. (2021) Development and Validation of a Machine Learned Turbulence Model. Energies 14:5, pages 1465.
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Xianwen Guo, Zhenhua Xia & Shiyi Chen. (2021) Computing mean fields with known Reynolds stresses at steady state. Theoretical and Applied Mechanics Letters 11:3, pages 100244.
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Sandeep Pandey & Jörg Schumacher. (2020) Reservoir computing model of two-dimensional turbulent convection. Physical Review Fluids 5:11.
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Yuhui Yin, Pu Yang, Yufei Zhang, Haixin Chen & Song Fu. (2020) Feature selection and processing of turbulence modeling based on an artificial neural network. Physics of Fluids 32:10.
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