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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Comparative analysis of machine learning methods for active flow control
Fabio Pino, Lorenzo Schena, Jean Rabault, et al.
Journal of Fluid Mechanics (2023) Vol. 958
Open Access | Times Cited: 46

Showing 1-25 of 46 citing articles:

Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions
Colin Vignon, Jean Rabault, Ricardo Vinuesa
Physics of Fluids (2023) Vol. 35, Iss. 3
Open Access | Times Cited: 78
Dynamic feature-based deep reinforcement learning for flow control of circular cylinder with sparse surface pressure sensing
Qiulei Wang, Lei Yan, Gang Hu, et al.
Journal of Fluid Mechanics (2024) Vol. 988
Open Access | Times Cited: 22
Active Flow Control for Drag Reduction Through Multi-agent Reinforcement Learning on a Turbulent Cylinder at $$Re_D=3900$$
Pol Suárez, Francisco Alcántara-Ávila, Arnau Miró, et al.
Flow Turbulence and Combustion (2025)
Open Access | Times Cited: 2
A review on deep reinforcement learning for fluid mechanics: An update
Jonathan Viquerat, Philippe Méliga, Aurélien Larcher, et al.
Physics of Fluids (2022) Vol. 34, Iss. 11
Open Access | Times Cited: 64
Effective control of two-dimensional Rayleigh–Bénard convection: Invariant multi-agent reinforcement learning is all you need
Colin Vignon, Jean Rabault, Joel Vasanth, et al.
Physics of Fluids (2023) Vol. 35, Iss. 6
Open Access | Times Cited: 29
Closed-loop plasma flow control of a turbulent cylinder wake flow using machine learning at Reynolds number of 28 000
Jie Chen, Haohua Zong, Huimin Song, et al.
Physics of Fluids (2024) Vol. 36, Iss. 1
Closed Access | Times Cited: 13
Active flow control for bluff body drag reduction using reinforcement learning with partial measurements
Chengwei Xia, Junjie Zhang, Eric C. Kerrigan, et al.
Journal of Fluid Mechanics (2024) Vol. 981
Open Access | Times Cited: 11
Deep reinforcement cross-domain transfer learning of active flow control for three-dimensional bluff body flow
Lei Yan, Qiulei Wang, Gang Hu, et al.
Journal of Computational Physics (2025), pp. 113893-113893
Closed Access | Times Cited: 1
Machine-learning flow control with few sensor feedback and measurement noise
Rodrigo Castellanos, Guy Y. Cornejo Maceda, Ignacio de la Fuente, et al.
Physics of Fluids (2022) Vol. 34, Iss. 4
Open Access | Times Cited: 32
Deep Reinforcement Learning for Flow Control Exploits Different Physics for Increasing Reynolds Number Regimes
Pau Varela, Pol Suárez, Francisco Alcántara-Ávila, et al.
Actuators (2022) Vol. 11, Iss. 12, pp. 359-359
Open Access | Times Cited: 31
Thermodynamics-informed neural network for recovering supercritical fluid thermophysical information from turbulent velocity data
Núria Masclans, Fernando Vázquez-Novoa, Marc Bernades, et al.
International Journal of Thermofluids (2023) Vol. 20, pp. 100448-100448
Open Access | Times Cited: 17
Adaptive control of transonic buffet and buffeting flow with deep reinforcement learning
Kai Ren, Chuanqiang Gao, Neng Xiong, et al.
Physics of Fluids (2024) Vol. 36, Iss. 1
Open Access | Times Cited: 6
Adjoint-based machine learning for active flow control
Xuemin Liu, Jonathan F. MacArt
Physical Review Fluids (2024) Vol. 9, Iss. 1
Open Access | Times Cited: 6
Jet mixing enhancement with Bayesian optimization, deep learning and persistent data topology
Yiqing Li, Bernd R. Noack, Tianyu Wang, et al.
Journal of Fluid Mechanics (2024) Vol. 991
Open Access | Times Cited: 6
Deep learning closure models for large-eddy simulation of flows around bluff bodies
Justin Sirignano, Jonathan F. MacArt
Journal of Fluid Mechanics (2023) Vol. 966
Open Access | Times Cited: 16
Reinforcement-learning-based control of convectively unstable flows
Da Xu, Mengqi Zhang
Journal of Fluid Mechanics (2023) Vol. 954
Open Access | Times Cited: 13
Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction
Chuangxin He, Xin Zeng, Peng Wang, et al.
Journal of Fluid Mechanics (2024) Vol. 978
Closed Access | Times Cited: 5
Establishment and validation of a relationship model between nozzle experiments and CFD results based on convolutional neural network
Yu Tao, Xiaoxiong Wu, Yang Yu, et al.
Aerospace Science and Technology (2023) Vol. 142, pp. 108694-108694
Closed Access | Times Cited: 12
Experimental analysis of heat and mass transfer in non-isothermal sloshing using a model-based inverse method
Pedro Afonso Marques, Alessia Simonini, Laura Peveroni, et al.
Applied Thermal Engineering (2023) Vol. 231, pp. 120871-120871
Open Access | Times Cited: 11
Optimal parallelization strategies for active flow control in deep reinforcement learning-based computational fluid dynamics
Wang Jia, Hang Xu
Physics of Fluids (2024) Vol. 36, Iss. 4
Open Access | Times Cited: 4
Shape optimization of the floating bridge pontoons based on the Fourier series expansion and deep reinforcement learning methods
Chenyu Lu, Jiabin Liu, Anxin Guo
Ocean Engineering (2025) Vol. 325, pp. 120792-120792
Closed Access
Closed-loop supersonic flow control with a high-speed experimental deep reinforcement learning framework
Haohua Zong, Yun Wu, Jinping Li, et al.
Journal of Fluid Mechanics (2025) Vol. 1009
Closed Access
Unsteady cylinder wakes from arbitrary bodies with differentiable physics-assisted neural network
Shuvayan Brahmachary, Nils Thuerey
Physical review. E (2024) Vol. 109, Iss. 5
Open Access | Times Cited: 3
Active flow control of square cylinder adaptive to wind direction using deep reinforcement learning
Lei Yan, Xingming Zhang, Jie Song, et al.
Physical Review Fluids (2024) Vol. 9, Iss. 9
Closed Access | Times Cited: 3
Deep Reinforcement Learning: A New Beacon for Intelligent Active Flow Control
Fangfang Xie, Changdong Zheng, Tingwei Ji, et al.
Aerospace Research Communications (2023) Vol. 1
Open Access | Times Cited: 8
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