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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:

A Review of Physics-Informed Machine Learning in Fluid Mechanics
Pushan Sharma, Wai Tong Chung, Bassem Akoush, et al.
Energies (2023) Vol. 16, Iss. 5, pp. 2343-2343
Open Access | Times Cited: 89

Showing 1-25 of 89 citing articles:

Multi-cavitation states diagnosis of the vortex pump using a combined DT-CWT-VMD and BO-LW-KNN based on motor current signals
Weitao Zeng, Peijian Zhou, Yanzhao Wu, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 19, pp. 30690-30705
Closed Access | Times Cited: 38
Analyzing magnetic dipole impact in fluid flow with endothermic/exothermic reactions: neural network simulation
R. S. Varun Kumar, K Chandan, Naman Sharma, et al.
Physica Scripta (2024) Vol. 99, Iss. 6, pp. 065215-065215
Closed Access | Times Cited: 27
Physics-informed neural networks for two-phase film boiling heat transfer
Darioush Jalili, Yasser Mahmoudi
International Journal of Heat and Mass Transfer (2025) Vol. 241, pp. 126680-126680
Open Access | Times Cited: 2
Can Artificial Intelligence Accelerate Fluid Mechanics Research?
Dimitris Drikakis, Filippos Sofos
Fluids (2023) Vol. 8, Iss. 7, pp. 212-212
Open Access | Times Cited: 29
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward
Mehdi Jabbari Zideh, Paroma Chatterjee, Anurag K. Srivastava
IEEE Access (2023) Vol. 12, pp. 4597-4617
Open Access | Times Cited: 28
The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering
Zhi‐Yong Wu, Huan Wang, Chang He, et al.
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 44, pp. 18178-18204
Closed Access | Times Cited: 25
Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges
Amer Farea, Olli Yli‐Harja, Frank Emmert‐Streib
AI (2024) Vol. 5, Iss. 3, pp. 1534-1557
Open Access | Times Cited: 14
Physics-informed neural networks for transonic flow around a cylinder with high Reynolds number
Xiang Ren, Peng Hu, Hua Su, et al.
Physics of Fluids (2024) Vol. 36, Iss. 3
Open Access | Times Cited: 13
Transfer learning through physics-informed neural networks for bubble growth in superheated liquid domains
Darioush Jalili, Mohammad Jadidi, Amir Keshmiri, et al.
International Journal of Heat and Mass Transfer (2024) Vol. 232, pp. 125940-125940
Open Access | Times Cited: 10
Elevator fault diagnosis based on digital twin and PINNs-e-RGCN
Qibing Wang, Laien Chen, Gang Xiao, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 8
Application of machine learning in adsorption energy storage using metal organic frameworks: A review
Nokubonga P. Makhanya, Michael Kumi, Charles Mbohwa, et al.
Journal of Energy Storage (2025) Vol. 111, pp. 115363-115363
Closed Access | Times Cited: 1
Application of physics-informed neural networks in fault diagnosis and fault-tolerant control design for electric vehicles: A review
Arslan Ahmed Amin, Amir Zaki Mubarak, Saba Waseem
Measurement (2025), pp. 116728-116728
Closed Access | Times Cited: 1
On the Preprocessing of Physics-informed Neural Networks: How to Better Utilize Data in Fluid Mechanics
Shengfeng Xu, Yuanjun Dai, Chang Yan, et al.
Journal of Computational Physics (2025), pp. 113837-113837
Closed Access | Times Cited: 1
PF-PINNs: Physics-informed neural networks for solving coupled Allen-Cahn and Cahn-Hilliard phase field equations
Nanxi Chen, S. Lucarini, Rujin Ma, et al.
Journal of Computational Physics (2025), pp. 113843-113843
Closed Access | Times Cited: 1
Numerical and machine learning based evaluation of ethylene glycol based hybrid nano-structured (TiO2-SWCNTs) fluid flow
Hijaz Ahmad, Kamel Guedri, Sohail Ahmad, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Using diffusion models for reducing spatiotemporal errors of deep learning based urban microclimate predictions at post-processing stage
Sepehrdad Tahmasebi, Geng Tian, Shaoxiang Qin, et al.
Physics of Fluids (2025) Vol. 37, Iss. 3
Closed Access | Times Cited: 1
Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview
Shuai Fu, Nicolas P. Avdelidis
Sensors (2023) Vol. 23, Iss. 19, pp. 8124-8124
Open Access | Times Cited: 17
Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
André Nicolle, Sili Deng, Matthias Ihme, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 3, pp. 597-620
Closed Access | Times Cited: 7
Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond
Ivan S. Maksymov
Energies (2023) Vol. 16, Iss. 14, pp. 5366-5366
Open Access | Times Cited: 16
Fluid Simulation on Neural Flow Maps
Yitong Deng, Hong-Xing Yu, Diyang Zhang, et al.
ACM Transactions on Graphics (2023) Vol. 42, Iss. 6, pp. 1-21
Open Access | Times Cited: 14
On the prediction of the turbulent flow behind cylinder arrays via Echo State Networks
Mohammad Sharifi Ghazijahani, Christian Cierpka
Machine Learning Science and Technology (2024) Vol. 5, Iss. 3, pp. 035005-035005
Open Access | Times Cited: 5
High-fidelity reconstruction of large-area damaged turbulent fields with a physically constrained generative adversarial network
Qinmin Zheng, Tianyi Li, Benteng Ma, et al.
Physical Review Fluids (2024) Vol. 9, Iss. 2
Closed Access | Times Cited: 4
Hygrothermal modeling in mass timber constructions: Recent advances and machine learning prospects
Sina Akhavan Shams, Hua Ge, Lin Wang
Journal of Building Engineering (2024) Vol. 96, pp. 110500-110500
Open Access | Times Cited: 4
Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
Jorge F. Urbán, Petros Stefanou, J. A. Pons
Journal of Computational Physics (2024), pp. 113656-113656
Open Access | Times Cited: 4
Flow dynamics in a vertical pipe with internal fins exposed to sunlight – A machine learning based evaluation of thermal signature
Assmaa Abd‐Elmonem, Zill E Shams, Mariam Imtiaz, et al.
Energy Conversion and Management X (2024), pp. 100846-100846
Open Access | Times Cited: 4
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