| Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence C Xie, J Wang, E Weinan Physical Review Fluids 5 (5), 054606, 2020 | 108 | 2020 |
| Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence C Xie, J Wang, H Li, M Wan, S Chen Physics of Fluids 31 (8), 2019 | 105 | 2019 |
| Deconvolutional artificial neural network models for large eddy simulation of turbulence Z Yuan, C Xie, J Wang Physics of Fluids 32 (11), 2020 | 83 | 2020 |
| Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence C Xie, Z Yuan, J Wang Physics of Fluids 32 (11), 2020 | 76 | 2020 |
| Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network C Xie, K Li, C Ma, J Wang Physical Review Fluids 4 (10), 104605, 2019 | 61 | 2019 |
| Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence C Xie, J Wang, K Li, C Ma Physical Review E 99 (5), 053113, 2019 | 61 | 2019 |
| Artificial neural network-based subgrid-scale models for large-eddy simulation of turbulence X Chenyu, Y Zelong, W Jianchun, W Minping, C Shiyi Chinese Journal of Theoretical and Applied Mechanics 53 (1), 1-16, 2021 | 41 | 2021 |
| Effect of shock waves on the statistics and scaling in compressible isotropic turbulence J Wang, M Wan, S Chen, C Xie, S Chen Physical Review E 97 (4), 043108, 2018 | 37 | 2018 |
| Cascades of temperature and entropy fluctuations in compressible turbulence J Wang, M Wan, S Chen, C Xie, LP Wang, S Chen Journal of Fluid Mechanics 867, 195-215, 2019 | 36 | 2019 |
| Artificial neural network-based spatial gradient models for large-eddy simulation of turbulence Y Wang, Z Yuan, C Xie, J Wang AIP Advances 11 (5), 2021 | 35 | 2021 |
| Spatially multi-scale artificial neural network model for large eddy simulation of compressible isotropic turbulence C Xie, J Wang, H Li, M Wan, S Chen AIP Advances 10 (1), 2020 | 35 | 2020 |
| Effects of compressibility and Atwood number on the single-mode Rayleigh-Taylor instability T Luo, J Wang, C Xie, M Wan, S Chen Physics of fluids 32 (1), 2020 | 35 | 2020 |
| Effect of flow topology on the kinetic energy flux in compressible isotropic turbulence J Wang, M Wan, S Chen, C Xie, Q Zheng, LP Wang, S Chen Journal of Fluid Mechanics 883, A11, 2020 | 34 | 2020 |
| A modified optimal LES model for highly compressible isotropic turbulence C Xie, J Wang, H Li, M Wan, S Chen Physics of Fluids 30 (6), 2018 | 34 | 2018 |
| Spatial artificial neural network model for subgrid-scale stress and heat flux of compressible turbulence C Xie, J Wang, H Li, M Wan, S Chen Theoretical and Applied Mechanics Letters 10 (1), 27-32, 2020 | 27 | 2020 |
| Dynamic iterative approximate deconvolution models for large-eddy simulation of turbulence Z Yuan, Y Wang, C Xie, J Wang Physics of Fluids 33 (8), 2021 | 24 | 2021 |
| Deconvolutional artificial-neural-network framework for subfilter-scale models of compressible turbulence Z Yuan, Y Wang, C Xie, J Wang Acta Mechanica Sinica 37 (12), 1773-1785, 2021 | 17 | 2021 |
| Artificial neural network approach for turbulence models: A local framework C Xie, X Xiong, J Wang Physical Review Fluids 6 (8), 084612, 2021 | 17 | 2021 |
| A dynamic spatial gradient model for the subgrid closure in large-eddy simulation of turbulence Y Wang, Z Yuan, C Xie, J Wang Physics of Fluids 33 (7), 2021 | 15 | 2021 |
| Retarding viscous Rayleigh-Taylor mixing by an optimized additional mode CY Xie, JJ Tao, ZL Sun, J Li Physical Review E 95 (2), 023109, 2017 | 14 | 2017 |
Jianchun WangSouthern University of Science and Technology, Shenzhen, ChinaVerified email at sustech.edu.cn
