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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-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations
Lei Yuan, Yi‐Qing Ni, Xiangyun Deng, et al.
Journal of Computational Physics (2022) Vol. 462, pp. 111260-111260
Open Access | Times Cited: 195

Showing 1-25 of 195 citing articles:

Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo, et al.
Journal of Scientific Computing (2022) Vol. 92, Iss. 3
Open Access | Times Cited: 1100
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics
Salah A. Faroughi, Nikhil M. Pawar, Célio Fernandes, et al.
Journal of Computing and Information Science in Engineering (2024) Vol. 24, Iss. 4
Closed Access | Times Cited: 49
A hybrid model-data-driven framework for inverse load identification of interval structures based on physics-informed neural network and improved Kalman filter algorithm
Yaru Liu, Lei Wang, Bing Feng Ng
Applied Energy (2024) Vol. 359, pp. 122740-122740
Closed Access | Times Cited: 30
A Review of Physics Informed Neural Networks for Multiscale Analysis and Inverse Problems
Dongjin Kim, Jae‐Wook Lee
Multiscale Science and Engineering (2024) Vol. 6, Iss. 1, pp. 1-11
Closed Access | Times Cited: 21
A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics
Chi Zhao, Feifei Zhang, Wenqiang Lou, et al.
Physics of Fluids (2024) Vol. 36, Iss. 10
Closed Access | Times Cited: 19
AT-PINN-HC: A refined time-sequential method incorporating hard-constraint strategies for predicting structural behavior under dynamic loads
Zhaolin Chen, S.K. Lai, Zhicheng Yang, et al.
Computer Methods in Applied Mechanics and Engineering (2025) Vol. 436, pp. 117691-117691
Closed Access | Times Cited: 4
Transfer Learning-Enhanced Finite Element-Integrated Neural Networks
Ning Zhang, Kunpeng Xu, Zhen‐Yu Yin, et al.
International Journal of Mechanical Sciences (2025), pp. 110075-110075
Open Access | Times Cited: 3
Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks
Van Truong Vo, Samad Noeiaghdam, Denis Sidorov, et al.
Computation (2025) Vol. 13, Iss. 1, pp. 13-13
Open Access | Times Cited: 2
An overview on deep learning-based approximation methods for partial differential equations
Christian Beck, Martin Hutzenthaler, Arnulf Jentzen, et al.
Discrete and Continuous Dynamical Systems - B (2022) Vol. 28, Iss. 6, pp. 3697-3746
Open Access | Times Cited: 54
Physics-Informed Neural Networks for Solving Forward and Inverse Problems in Complex Beam Systems
Taniya Kapoor, Hongrui Wang, Alfredo Núñez, et al.
IEEE Transactions on Neural Networks and Learning Systems (2023) Vol. 35, Iss. 5, pp. 5981-5995
Open Access | Times Cited: 40
Enhancing PINNs for solving PDEs via adaptive collocation point movement and adaptive loss weighting
Jie Hou, Ying Li, Shihui Ying
Nonlinear Dynamics (2023) Vol. 111, Iss. 16, pp. 15233-15261
Closed Access | Times Cited: 37
Adaptive transfer learning for PINN
Yang Liu, Liu Wen, Xunshi Yan, et al.
Journal of Computational Physics (2023) Vol. 490, pp. 112291-112291
Open Access | Times Cited: 33
A swarming neural network computing approach to solve the Zika virus model
Zulqurnain Sabir, Shahid Ahmad Bhat, Muhammad Asif Zahoor Raja, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106924-106924
Open Access | Times Cited: 30
Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics
Wenzhao Wu, Mitchell Daneker, Matthew A. Jolley, et al.
Applied Mathematics and Mechanics (2023) Vol. 44, Iss. 7, pp. 1039-1068
Open Access | Times Cited: 29
NAS-PINN: Neural architecture search-guided physics-informed neural network for solving PDEs
Yifan Wang, Linlin Zhong
Journal of Computational Physics (2023) Vol. 496, pp. 112603-112603
Open Access | Times Cited: 28
Reliability assessment of stochastic dynamical systems using physics informed neural network based PDEM
Sourav Das, Solomon Tesfamariam
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109849-109849
Closed Access | Times Cited: 27
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: 26
Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations
Zhi‐Yong Zhang, Hui Zhang, Lisheng Zhang, et al.
Journal of Computational Physics (2023) Vol. 492, pp. 112415-112415
Open Access | Times Cited: 25
AT-PINN: Advanced time-marching physics-informed neural network for structural vibration analysis
Zhaolin Chen, S.K. Lai, Zhichun Yang
Thin-Walled Structures (2023) Vol. 196, pp. 111423-111423
Closed Access | Times Cited: 24
Physics-Informed neural network solver for numerical analysis in geoengineering
Xiaoxuan Chen, Pin Zhang, Zhen‐Yu Yin
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2024) Vol. 18, Iss. 1, pp. 33-51
Open Access | Times Cited: 12
Physics informed neural networks for an inverse problem in peridynamic models
Fabio V. Difonzo, L. Lopez, Sabrina Francesca Pellegrino
Engineering With Computers (2024)
Open Access | Times Cited: 10
Physics-informed Neural Network Supported Wiener Process for Degradation Modeling and Reliability Prediction
Zewen He, Shaoping Wang, Jian Shi, et al.
Reliability Engineering & System Safety (2025), pp. 110906-110906
Closed Access | Times Cited: 1
Energy-based PINNs using the element integral approach and their enhancement for solid mechanics problems
Junwei Chen, Jian-Xiang Ma, Zhihe Zhao, et al.
International Journal of Solids and Structures (2025), pp. 113315-113315
Closed Access | Times Cited: 1
Application of Physics-Informed Neural Networks to predict concentration profiles in gradient liquid chromatography
Filip Rękas, Marcin Chutkowski, Krzysztof Kaczmarski
Journal of Chromatography A (2025) Vol. 1748, pp. 465831-465831
Closed Access | Times Cited: 1
Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis
Ariel Neufeld, Sizhou Wu
Stochastic Partial Differential Equations Analysis and Computations (2025)
Closed Access | Times Cited: 1
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