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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:

Unsteady aerodynamic reduced-order modeling based on machine learning across multiple airfoils
Kai Li, Jiaqing Kou, Weiwei Zhang
Aerospace Science and Technology (2021) Vol. 119, pp. 107173-107173
Closed Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: A state-of-the-art overview
Qi Liu, Yong Xu, Jürgen Kurths, et al.
Chaos An Interdisciplinary Journal of Nonlinear Science (2022) Vol. 32, Iss. 6
Open Access | Times Cited: 43
MHA-Net: Multi-source heterogeneous aerodynamic data fusion neural network embedding reduced-dimension features
Chenjia Ning, Weiwei Zhang
Aerospace Science and Technology (2024) Vol. 145, pp. 108908-108908
Closed Access | Times Cited: 10
Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach
Michele Lazzara, Max Chevalier, Michele Colombo, et al.
Aerospace Science and Technology (2022) Vol. 126, pp. 107629-107629
Open Access | Times Cited: 34
Towards high-accuracy deep learning inference of compressible flows over aerofoils
Li-Wei Chen, Nils Thuerey
Computers & Fluids (2022) Vol. 250, pp. 105707-105707
Closed Access | Times Cited: 33
An enhanced hybrid deep neural network reduced-order model for transonic buffet flow prediction
Ziyang Liu, Renkun Han, Miao Zhang, et al.
Aerospace Science and Technology (2022) Vol. 126, pp. 107636-107636
Closed Access | Times Cited: 29
Prediction of transonic flow over supercritical airfoils using geometric-encoding and deep-learning strategies
Zhiwen Deng, Jing Wang, Hongsheng Liu, et al.
Physics of Fluids (2023) Vol. 35, Iss. 7
Open Access | Times Cited: 16
Prediction of internal and external flow with sparse convolution neural network: A computationally effective reduced-order model
Jiang-Zhou Peng, Nadine Aubry, Yue Hua, et al.
Physics of Fluids (2023) Vol. 35, Iss. 2
Closed Access | Times Cited: 15
Mechanism of single-mode panel flutter in low supersonic flow
Liuqing Ye, Kun Ye, Ke Jin, et al.
Aerospace Science and Technology (2024) Vol. 147, pp. 109002-109002
Closed Access | Times Cited: 6
Research and Application of ROM Based on Res-PINNs Neural Network in Fluid System
Yuhao Liu, Junjie Hou, Ping Wei, et al.
Symmetry (2025) Vol. 17, Iss. 2, pp. 163-163
Open Access
TurbineNet: Advancing tidal turbine blade hydrodynamic performance prediction with neural networks
Jian Xu, Longyan Wang, Jianping Yuan, et al.
Physics of Fluids (2025) Vol. 37, Iss. 2
Closed Access
Comparative Analysis of Reinforcement Learning Agents for Optimizing Airfoil Shapes
Harsh H. Sawant, Rahul Gujar, Neeta Amol Mandhare, et al.
International Journal for Numerical Methods in Fluids (2025)
Closed Access
On the construction of a mode based reduced order model for a moving store
Nicholas Peters, Andrew M. Wissink, John A. Ekaterinaris
Aerospace Science and Technology (2022) Vol. 123, pp. 107484-107484
Closed Access | Times Cited: 26
Quick intention identification of an enemy aerial target through information classification processing
Yinhan Wang, Jiang Wang, Shipeng Fan, et al.
Aerospace Science and Technology (2022) Vol. 132, pp. 108005-108005
Open Access | Times Cited: 22
A cost-effective CNN-BEM coupling framework for design optimization of horizontal axis tidal turbine blades
Jian Xu, Longyan Wang, Jianping Yuan, et al.
Energy (2023) Vol. 282, pp. 128707-128707
Closed Access | Times Cited: 14
Temporal predictions of periodic flows using a mesh transformation and deep learning-based strategy
Zhiwen Deng, Hongsheng Liu, Beiji Shi, et al.
Aerospace Science and Technology (2022) Vol. 134, pp. 108081-108081
Closed Access | Times Cited: 15
Data-based autonomously discovering method for nonlinear aerodynamic force of quasi-flat plate
Teng Ma, Wei Cui, Ting-Ting Gao, et al.
Physics of Fluids (2023) Vol. 35, Iss. 2
Closed Access | Times Cited: 8
Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars Helicopter
Pengyue Zhao, Xifeng Gao, Bo Zhao, et al.
Aerospace (2023) Vol. 10, Iss. 7, pp. 614-614
Open Access | Times Cited: 8
Dynamic stall modeling of wind turbine blade sections based on a data-knowledge fusion method
Zijie Shi, Chuanqiang Gao, Zihao Dou, et al.
Energy (2024) Vol. 305, pp. 132234-132234
Closed Access | Times Cited: 2
A hybrid reduced-order model combing deep learning for unsteady flow
Xuyi Jia, Chunna Li, Wen Ji, et al.
Physics of Fluids (2022) Vol. 34, Iss. 9
Closed Access | Times Cited: 13
Artificial Intelligence Design for Ship Structures: A Variant Multiple-Input Neural Network-Based Ship Resistance Prediction
Ao Yu, Yunbo Li, Jiaye Gong, et al.
Journal of Mechanical Design (2022) Vol. 144, Iss. 9
Closed Access | Times Cited: 12
Deep learning predictions of unsteady aerodynamic loads on an airfoil model pitched over the entire operating range
Ayman Mohamed, David Wood
Physics of Fluids (2023) Vol. 35, Iss. 5
Open Access | Times Cited: 7
Aircraft Closed-Loop Dynamic System Identification in the Entire Flight Envelope Range Based on Deep Learning
Zhigang Wang, Aijun Li, Yi Mi, et al.
Journal of Aerospace Engineering (2024) Vol. 37, Iss. 6
Closed Access | Times Cited: 1
A novel deep learning approach for flow field prediction around airfoils leveraging computational space representation
Paride Caraccio, Guido Marseglia, Agostino Lauria, et al.
Physics of Fluids (2024) Vol. 36, Iss. 10
Closed Access | Times Cited: 1
Accelerating unsteady aerodynamic simulations using predictive reduced-order modeling
Z. Y. Li, Ping He
Aerospace Science and Technology (2023) Vol. 139, pp. 108412-108412
Open Access | Times Cited: 4
Data Puncturing and Training Strategies for Cost-Efficient Surrogate Modeling of Rotorcraft Airfoil Aerodynamics
Koushik Marepally, Nick Paternostro, Bumseok Lee, et al.
AIAA SCITECH 2022 Forum (2023)
Closed Access | Times Cited: 3
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