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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:

Inverse flow prediction using ensemble PINNs and uncertainty quantification
Jerol Soibam, Ioanna Aslanidou, Konstantinos Kyprianidis, et al.
International Journal of Heat and Mass Transfer (2024) Vol. 226, pp. 125480-125480
Open Access | Times Cited: 13

Showing 13 citing articles:

From PINNs to PIKANs: recent advances in physics-informed machine learning
Juan Diego Toscano, Vivek Oommen, Alan John Varghese, et al.
Machine learning for computational science and engineering (2025) Vol. 1, Iss. 1
Closed Access | Times Cited: 5
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
Advances in Numerical Modeling for Heat Transfer and Thermal Management: A Review of Computational Approaches and Environmental Impacts
Łukasz Łach, Dmytro Svyetlichnyy
Energies (2025) Vol. 18, Iss. 5, pp. 1302-1302
Open Access | Times Cited: 1
Uncertainty quantification for noisy inputs–outputs in physics-informed neural networks and neural operators
Zongren Zou, Xuhui Meng, George Em Karniadakis
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 433, pp. 117479-117479
Closed Access | Times Cited: 8
Physics-informed neural network simulation of conjugate heat transfer in manifold microchannel heat sinks for high-power IGBT cooling
Xiangzhi Zhang, Chaofan Tu, Yuying Yan
International Communications in Heat and Mass Transfer (2024) Vol. 159, pp. 108036-108036
Open Access | Times Cited: 4
Status Prediction in a Fuzzy Conflict Model with Chaotic Behavior Using a Physics-Informed Neural Network
Gwi-Man Bak, Young-Chul Bae
International Journal of Fuzzy Systems (2025)
Closed Access
A novel machine learning method for real-time dynamic analysis of tensegrity flexible multibody systems
Ningning Song, Mingji Wang, Xinwei Wang, et al.
Nonlinear Dynamics (2025)
Closed Access
Extended multiphysics-informed neural network for conjugate heat transfer problems
Jongmok Lee, Seungmin Shin, Ho Jin Choi, et al.
International Journal of Heat and Mass Transfer (2025) Vol. 246, pp. 127098-127098
Closed Access
Physics-Informed Neural Networks in Polymers: A Review
Ivan Malashin, В С Тынченко, Andrei Gantimurov, et al.
Polymers (2025) Vol. 17, Iss. 8, pp. 1108-1108
Open Access
A data-physic driven method for gear fault diagnosis using PINN and pseudo-dynamic features
Yikun Yang, Xifeng Wang, Jinfeng Li, et al.
Measurement (2024) Vol. 236, pp. 115124-115124
Closed Access | Times Cited: 2
A Physics-Informed Neural Network Approach for Solving the Engineering Eigenvalue Problem
Seong-Joon Yoo, Min Seo Kang, Heonjun Yoon, et al.
(2024)
Closed Access | Times Cited: 1
A Physics-Informed Neural Network Approach for Solving the Engineering Eigenvalue Problem
Seong-Joon Yoo, Min Seo Kang, Heonjun Yoon, et al.
(2024)
Closed Access | Times Cited: 1
Hierarchical Bayesian modeling for Inverse Uncertainty Quantification of system thermal-hydraulics code using critical flow experimental data
Ziyu Xie, Chen Wang, Xu Wu
International Journal of Heat and Mass Transfer (2024) Vol. 239, pp. 126489-126489
Closed Access
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