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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:

Machine learning-based surrogate modeling approaches for fixed-wing store separation
Nicholas Peters, Andrew M. Wissink, John A. Ekaterinaris
Aerospace Science and Technology (2023) Vol. 133, pp. 108150-108150
Closed Access | Times Cited: 23

Showing 23 citing articles:

Flow3DNet: A deep learning framework for efficient simulation of three-dimensional wing flow fields
Kuijun Zuo, Zhengyin Ye, Xianxu Yuan, et al.
Aerospace Science and Technology (2025), pp. 109991-109991
Closed Access | Times Cited: 1
Multi-fidelity neural network-based aerodynamic optimization framework for propeller design in electric aircraft
Xiaojing Wu, Zijun Zuo, Long Ma, et al.
Aerospace Science and Technology (2024) Vol. 146, pp. 108963-108963
Closed Access | Times Cited: 13
A deep learning super-resolution model for turbulent image upscaling and its application to shock wave–boundary layer interaction
Filippos Sofos, Dimitris Drikakis, Ioannis W. Kokkinakis, et al.
Physics of Fluids (2024) Vol. 36, Iss. 2
Open Access | Times Cited: 12
An accuracy-enhanced transonic flow prediction method fusing deep learning and a reduced-order model
Xuyi Jia, Chunlin Gong, Wen Ji, et al.
Physics of Fluids (2024) Vol. 36, Iss. 5
Open Access | Times Cited: 9
Fast flow field prediction approach of supersonic inlet in wide operating range based on deep learning
Jiaxiang Zhong, Feng Qu, Di Sun, et al.
Aerospace Science and Technology (2024) Vol. 146, pp. 108955-108955
Closed Access | Times Cited: 6
A unified deep learning model for thermoacoustic instability detection in swirl premixed flames based on spatial-temporal features
Xiaojing Li, Chunyu Liu, Dezhi Zhou, et al.
Aerospace Science and Technology (2025), pp. 109994-109994
Closed Access
Efficient machine learning method for supercritical combustion: Predicting real-fluid properties and chemical ODEs
Yuqing Cai, Ruishen Yang, Han Li, et al.
Aerospace Science and Technology (2025) Vol. 159, pp. 110034-110034
Closed Access
A Data-Driven Approach in Real-Time Trajectory Prediction of Store Release From Cavity
A. Das, Errol Hale, Pier Marzocca, et al.
AIAA SCITECH 2022 Forum (2025)
Closed Access
Design and Application of the Sage Surrogate Modeling Software
Andrew L. Kaminsky, Alec M. House, L Jensen, et al.
AIAA SCITECH 2022 Forum (2025)
Closed Access
Deep neural network for learning wave scattering and interference of underwater acoustics
Wrik Mallik, Rajeev K. Jaiman, Jasmin Jelovica
Physics of Fluids (2024) Vol. 36, Iss. 1
Open Access | Times Cited: 3
A fast prediction model of blade flutter in turbomachinery based on graph convolutional neural network
Yupeng Liu, Yunzhu Li, Liangliang Li, et al.
Aerospace Science and Technology (2024) Vol. 148, pp. 109119-109119
Closed Access | Times Cited: 3
A comprehensive review of water entry/exit of lifeboats and occupant safety
Di Wang, Ningge Fan, Binyi Liang, et al.
Ocean Engineering (2024) Vol. 310, pp. 118768-118768
Closed Access | Times Cited: 2
A variable fidelity approach for predicting aerodynamic wall quantities of hypersonic vehicles using the ConvNeXt Encoder-Decoder framework
Yuxin Yang, Shaobo Yao, Youtao Xue, et al.
Aerospace Science and Technology (2024), pp. 109605-109605
Closed Access | Times Cited: 2
A ML strategy for the identification of optimal LPT design region and related blade shape
Daniele Petronio, Pietro Paliotta, Davide Lengani, et al.
Aerospace Science and Technology (2024) Vol. 148, pp. 109118-109118
Open Access | Times Cited: 1
The source localization and dynamical evolution of axisymmetric screech modes in underexpanded supersonic jets
Hu Li, Yong Luo, Shuaibin Han, et al.
Aerospace Science and Technology (2023) Vol. 140, pp. 108427-108427
Closed Access | Times Cited: 4
Progress and prospects of artificial intelligence development and applications in supersonic flow and combustion
Jialing Le, Maotao Yang, Mingming Guo, et al.
Progress in Aerospace Sciences (2024), pp. 101046-101046
Closed Access | Times Cited: 1
Development of POD-based Reduced Order Models applied to shallow water equations using augmented Riemann solvers
P. Solán-Fustero, J.L. Gracia, Adrián Navas-Montilla, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 410, pp. 116038-116038
Open Access | Times Cited: 3
A data-driven reduced-order model for rotor optimization
Nicholas Peters, Christopher J. Silva, John A. Ekaterinaris
Wind energy science (2023) Vol. 8, Iss. 7, pp. 1201-1223
Open Access | Times Cited: 2
Vibration Characteristics of a Pretwisted Multi-Blade-Shaft System with Blade Stiffness Mismatch
Su Ju, Lin Yu, Tianyu Zhao
Aerospace Science and Technology (2024), pp. 109518-109518
Closed Access
Experimental Investigation of Non-synchronous Vibration and Its Mechanism Analysis by Using a Cantilever Beam-Like Compressor Cascade
Chunlong Tan, Hangshan Gao, P. R. Li, et al.
Aerospace Science and Technology (2024), pp. 109626-109626
Closed Access
Fast Aerodynamic Analysis Method for Three-Dimensional Morphing Wings Based on Deep Learning
R. Xie, Zhiqiang Wan, De Yan, et al.
Aerospace Science and Technology (2024) Vol. 155, pp. 109690-109690
Open Access
Prediction of pressure distribution and aerodynamic coefficients for a variable-sweep wing
Yuqi Lei, Xiaomin An, Yihua Pan, et al.
Aerospace Science and Technology (2024), pp. 109706-109706
Closed Access
An accuracy-enhanced transonic flow prediction method fusing deep learning and reduced-order model
Xuyi Jia, Chunlin Gong, Wen Ji, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 1
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