| Machine learning methods for turbulence modeling in subsonic flows around airfoils L Zhu, W Zhang, J Kou, Y Liu Physics of Fluids 31 (1), 2019 | 314 | 2019 |
| An improved criterion to select dominant modes from dynamic mode decomposition J Kou, W Zhang European Journal of Mechanics-B/Fluids 62, 109-129, 2017 | 218 | 2017 |
| Mechanism of frequency lock-in in vortex-induced vibrations at low Reynolds numbers W Zhang, X Li, Z Ye, Y Jiang Journal of Fluid Mechanics 783, 72-102, 2015 | 206 | 2015 |
| Efficient method for limit cycle flutter analysis based on nonlinear aerodynamic reduced-order models W Zhang, B Wang, Z Ye, J Quan AIAA journal 50 (5), 1019-1028, 2012 | 164 | 2012 |
| Supersonic flutter analysis based on a local piston theory WW Zhang, ZY Ye, CA Zhang, F Liu AIAA Journal 47 (10), 2321-2328, 2009 | 138 | 2009 |
| Deep neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers K Li, J Kou, W Zhang Nonlinear Dynamics 96, 2157-2177, 2019 | 127 | 2019 |
| Data-driven modeling for unsteady aerodynamics and aeroelasticity J Kou, W Zhang Progress in Aerospace Sciences 125, 100725, 2021 | 121 | 2021 |
| Mechanism of frequency lock-in in transonic buffeting flow C Gao, W Zhang, X Li, Y Liu, J Quan, Z Ye, Y Jiang Journal of Fluid Mechanics 818, 528-561, 2017 | 104 | 2017 |
| Two better loosely coupled solution algorithms of CFD based aeroelastic simulation W Zhang, Y Jiang, Z Ye Engineering Applications of Computational Fluid Mechanics 1 (4), 253-262, 2007 | 88 | 2007 |
| The lowest Reynolds number of vortex-induced vibrations J Kou, W Zhang, Y Liu, X Li Physics of Fluids 29 (4), 2017 | 86 | 2017 |
| A reduced-order model for compressible flows with buffeting condition using higher order dynamic mode decomposition with a mode selection criterion J Kou, S Le Clainche, W Zhang Physics of Fluids 30 (1), 2018 | 80 | 2018 |
| Active control of transonic buffet flow C Gao, W Zhang, J Kou, Y Liu, Z Ye Journal of Fluid Mechanics 824, 312-351, 2017 | 76 | 2017 |
| Multi-kernel neural networks for nonlinear unsteady aerodynamic reduced-order modeling J Kou, W Zhang Aerospace Science and Technology 67, 309-326, 2017 | 75 | 2017 |
| A hybrid reduced-order framework for complex aeroelastic simulations J Kou, W Zhang Aerospace science and technology 84, 880-894, 2019 | 74 | 2019 |
| Mode competition in galloping of a square cylinder at low Reynolds number X Li, Z Lyu, J Kou, W Zhang Journal of Fluid Mechanics 867, 516-555, 2019 | 73 | 2019 |
| Turbulence closure for high Reynolds number airfoil flows by deep neural networks L Zhu, W Zhang, X Sun, Y Liu, X Yuan Aerospace Science and Technology 110, 106452, 2021 | 72 | 2021 |
| Transonic aeroelasticity: A new perspective from the fluid mode C Gao, W Zhang Progress in Aerospace Sciences 113, 100596, 2020 | 69 | 2020 |
| Benchmark aerodynamic shape optimization with the POD-based CST airfoil parametric method X Wu, W Zhang, X Peng, Z Wang Aerospace Science and Technology 84, 632-640, 2019 | 66 | 2019 |
| Prospect of artificial intelligence empowered fluid mechanics Z Weiwei, K Jiaqing, L Yilang Acta Aeronautica et Astronautica Sinica 42 (4), 524689, 2021 | 65 | 2021 |
| Control law design for transonic aeroservoelasticity W Zhang, Z Ye Aerospace Science and Technology 11 (2-3), 136-145, 2007 | 65 | 2007 |
Jiaqing Kou (寇家庆)Professor, Northwestern Polytechnical UniversityVerified email at nwpu.edu.cn
