Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Xie, Chenyue
Wang, Jianchun
Li, Hui
Wan, Minping
and
Chen, Shiyi
2019.
Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence.
Physics of Fluids,
Vol. 31,
Issue. 8,
Xie, Chenyue
Li, Ke
Ma, Chao
and
Wang, Jianchun
2019.
Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network.
Physical Review Fluids,
Vol. 4,
Issue. 10,
Yuan, Zelong
Xie, Chenyue
and
Wang, Jianchun
2020.
Deconvolutional artificial neural network models for large eddy simulation of turbulence.
Physics of Fluids,
Vol. 32,
Issue. 11,
Prat, Alvaro
Sautory, Theophile
and
Navarro-Martinez, S.
2020.
A Priori Sub-grid Modelling Using Artificial Neural Networks.
International Journal of Computational Fluid Dynamics,
Vol. 34,
Issue. 6,
p.
397.
Vaddireddy, Harsha
Rasheed, Adil
Staples, Anne E.
and
San, Omer
2020.
Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data.
Physics of Fluids,
Vol. 32,
Issue. 1,
Maulik, Romit
Fukami, Kai
Ramachandra, Nesar
Fukagata, Koji
and
Taira, Kunihiko
2020.
Probabilistic neural networks for fluid flow surrogate modeling and data recovery.
Physical Review Fluids,
Vol. 5,
Issue. 10,
Xie, Chenyue
Wang, Jianchun
and
E, Weinan
2020.
Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence.
Physical Review Fluids,
Vol. 5,
Issue. 5,
Pandey, Sandeep
Schumacher, Jörg
and
Sreenivasan, Katepalli R.
2020.
A perspective on machine learning in turbulent flows.
Journal of Turbulence,
Vol. 21,
Issue. 9-10,
p.
567.
Yi, Haibo
2020.
Efficient architecture for improving differential equations based on normal equation method in deep learning.
Alexandria Engineering Journal,
Vol. 59,
Issue. 4,
p.
2491.
Kim, Minwoo
Lim, Jiseop
Kim, Seungtae
Jee, Solkeun
and
Park, Donghun
2020.
Assessment of the wall-adapting local eddy-viscosity model in transitional boundary layer.
Computer Methods in Applied Mechanics and Engineering,
Vol. 371,
Issue. ,
p.
113287.
Maulik, Romit
San, Omer
and
Jacob, Jamey D.
2020.
Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers.
Physica D: Nonlinear Phenomena,
Vol. 406,
Issue. ,
p.
132409.
Ahmed, Shady E.
San, Omer
Kara, Kursat
Younis, Rami
and
Rasheed, Adil
2020.
Interface learning of multiphysics and multiscale systems.
Physical Review E,
Vol. 102,
Issue. 5,
Runchal, Akshai Kumar
and
Rao, Madhukar M.
2020.
50 Years of CFD in Engineering Sciences.
p.
779.
Wang, Bo
and
Wang, Jingtao
2021.
Application of Artificial Intelligence in Computational Fluid Dynamics.
Industrial & Engineering Chemistry Research,
Vol. 60,
Issue. 7,
p.
2772.
Semlitsch, Bernhard
and
Mihăescu, Mihai
2021.
Evaluation of Injection Strategies in Supersonic Nozzle Flow.
Aerospace,
Vol. 8,
Issue. 12,
p.
369.
Portwood, G. D.
Nadiga, B. T.
Saenz, J. A.
and
Livescu, D.
2021.
Interpreting neural network models of residual scalar flux.
Journal of Fluid Mechanics,
Vol. 907,
Issue. ,
Font, Bernat
Weymouth, Gabriel D.
Nguyen, Vinh-Tan
and
Tutty, Owen R.
2021.
Deep learning of the spanwise-averaged Navier–Stokes equations.
Journal of Computational Physics,
Vol. 434,
Issue. ,
p.
110199.
Jiang, Chao
Vinuesa, Ricardo
Chen, Ruilin
Mi, Junyi
Laima, Shujin
and
Li, Hui
2021.
An interpretable framework of data-driven turbulence modeling using deep neural networks.
Physics of Fluids,
Vol. 33,
Issue. 5,
Daniel, Thomas
Casenave, Fabien
Akkari, Nissrine
and
Ryckelynck, David
2021.
Data Augmentation and Feature Selection for Automatic Model Recommendation in Computational Physics.
Mathematical and Computational Applications,
Vol. 26,
Issue. 1,
p.
17.
Maulik, Romit
Sharma, Himanshu
Patel, Saumil
Lusch, Bethany
and
Jennings, Elise
2021.
A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations.
Computers & Fluids,
Vol. 227,
Issue. ,
p.
104777.