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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:

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease
Shengze Cai, He Li, Fuyin Zheng, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 13
Open Access | Times Cited: 88

Showing 1-25 of 88 citing articles:

Physics-informed neural networks (PINNs) for fluid mechanics: a review
Shengze Cai, Zhiping Mao, Zhicheng Wang, et al.
Acta Mechanica Sinica (2021) Vol. 37, Iss. 12, pp. 1727-1738
Closed Access | Times Cited: 987
Parallel physics-informed neural networks via domain decomposition
Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
Journal of Computational Physics (2021) Vol. 447, pp. 110683-110683
Open Access | Times Cited: 259
Analyses of internal structures and defects in materials using physics-informed neural networks
Enrui Zhang, Ming Dao, George Em Karniadakis, et al.
Science Advances (2022) Vol. 8, Iss. 7
Open Access | Times Cited: 204
Uncovering near-wall blood flow from sparse data with physics-informed neural networks
Amirhossein Arzani, Jianxun Wang, Roshan M. D’Souza
Physics of Fluids (2021) Vol. 33, Iss. 7
Open Access | Times Cited: 194
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
Han Gao, Matthew J. Zahr, Jianxun Wang
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 390, pp. 114502-114502
Open Access | Times Cited: 188
PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
Pu Ren, Chengping Rao, Yang Liu, et al.
Computer Methods in Applied Mechanics and Engineering (2021) Vol. 389, pp. 114399-114399
Open Access | Times Cited: 154
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
Deep recurrent optical flow learning for particle image velocimetry data
Christian Lagemann, Kai Lagemann, Sach Mukherjee, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 7, pp. 641-651
Closed Access | Times Cited: 81
Patient‐Specific Organoid and Organ‐on‐a‐Chip: 3D Cell‐Culture Meets 3D Printing and Numerical Simulation
Fuyin Zheng, Yuminghao Xiao, Hui Liu, et al.
Advanced Biology (2021) Vol. 5, Iss. 6
Open Access | Times Cited: 66
MFLP-PINN: A physics-informed neural network for multiaxial fatigue life prediction
GaoYuan He, Yongxiang Zhao, ChuLiang Yan
European Journal of Mechanics - A/Solids (2022) Vol. 98, pp. 104889-104889
Closed Access | Times Cited: 56
Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows
Kimberly A. Stevens, Shengze Cai, Antonio Ladrón-de-Guevara, et al.
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 14
Open Access | Times Cited: 40
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear partial differential equations
Jinshuai Bai, Guirong Liu, Ashish Gupta, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 415, pp. 116290-116290
Open Access | Times Cited: 36
Digital twin of wind farms via physics-informed deep learning
Jincheng Zhang, Xiaowei Zhao
Energy Conversion and Management (2023) Vol. 293, pp. 117507-117507
Open Access | Times Cited: 27
Advances in In Vitro Models of Neuromuscular Junction: Focusing on Organ‐on‐a‐Chip, Organoids, and Biohybrid Robotics
Yubing Leng, Xiaorui Li, Fuyin Zheng, et al.
Advanced Materials (2023) Vol. 35, Iss. 41
Closed Access | Times Cited: 26
Microsystem Advances through Integration with Artificial Intelligence
Hsieh‐Fu Tsai, Soumyajit Podder, Pin‐Yuan Chen
Micromachines (2023) Vol. 14, Iss. 4, pp. 826-826
Open Access | Times Cited: 24
AI-enhanced biomedical micro/nanorobots in microfluidics
Hui Dong, Jiawen Lin, Yihui Tao, et al.
Lab on a Chip (2024) Vol. 24, Iss. 5, pp. 1419-1440
Open Access | Times Cited: 16
From Organ-on-a-Chip to Human-on-a-Chip: A Review of Research Progress and Latest Applications
Yisha Huang, Tong Liu, Qi Huang, et al.
ACS Sensors (2024) Vol. 9, Iss. 7, pp. 3466-3488
Closed Access | Times Cited: 15
Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning
Jincheng Zhang, Xiaowei Zhao
Applied Energy (2021) Vol. 300, pp. 117390-117390
Open Access | Times Cited: 49
Flow field tomography with uncertainty quantification using a Bayesian physics-informed neural network
Joseph P. Molnar, Samuel J. Grauer
Measurement Science and Technology (2022) Vol. 33, Iss. 6, pp. 065305-065305
Open Access | Times Cited: 36
EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks
Clara Herrero Martin, Alon Oved, Rasheda A. Chowdhury, et al.
Frontiers in Cardiovascular Medicine (2022) Vol. 8
Open Access | Times Cited: 33
Physical laws meet machine intelligence: current developments and future directions
Temoor Muther, Amirmasoud Kalantari Dahaghi, Fahad I. Syed, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 7, pp. 6947-7013
Closed Access | Times Cited: 30
Multiaxial fatigue life prediction using physics-informed neural networks with sensitive features
GaoYuan He, Yongxiang Zhao, ChuLiang Yan
Engineering Fracture Mechanics (2023) Vol. 289, pp. 109456-109456
Closed Access | Times Cited: 21
A physics‐informed generative adversarial network framework for multiaxial fatigue life prediction
GaoYuan He, Yongxiang Zhao, ChuLiang Yan
Fatigue & Fracture of Engineering Materials & Structures (2023) Vol. 46, Iss. 10, pp. 4036-4052
Open Access | Times Cited: 17
Multiscale Physics-Informed Neural Network Framework to Capture Stochastic Thin-Film Deposition
Donovan Chaffart, Yue Yuan, Luis Ricardez‐Sandoval
The Journal of Physical Chemistry C (2024) Vol. 128, Iss. 9, pp. 3733-3750
Closed Access | Times Cited: 7
Studies of the retinal microcirculation using human donor eyes and high-resolution clinical imaging: Insights gained to guide future research in diabetic retinopathy
Chandrakumar Balaratnasingam, Dong An, Martin Hein, et al.
Progress in Retinal and Eye Research (2022) Vol. 94, pp. 101134-101134
Closed Access | Times Cited: 27
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