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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:

Non-invasive inference of thrombus material properties with physics-informed neural networks
Minglang Yin, Xiaoning Zheng, Jay D. Humphrey, et al.
Computer Methods in Applied Mechanics and Engineering (2020) Vol. 375, pp. 113603-113603
Open Access | Times Cited: 143

Showing 1-25 of 143 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: 940
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: 250
A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials
Somdatta Goswami, Minglang Yin, Yue Yu, et al.
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 391, pp. 114587-114587
Open Access | Times Cited: 221
Self-adaptive loss balanced Physics-informed neural networks
Zixue Xiang, Wei Peng, Xü Liu, et al.
Neurocomputing (2022) Vol. 496, pp. 11-34
Open Access | Times Cited: 211
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: 189
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: 182
Data-driven modeling for unsteady aerodynamics and aeroelasticity
Jiaqing Kou, Weiwei Zhang
Progress in Aerospace Sciences (2021) Vol. 125, pp. 100725-100725
Closed Access | Times Cited: 149
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: 149
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, et al.
SIAM Journal on Scientific Computing (2022) Vol. 44, Iss. 5, pp. A3158-A3182
Open Access | Times Cited: 85
Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems
Minglang Yin, Enrui Zhang, Yue Yu, et al.
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 402, pp. 115027-115027
Open Access | Times Cited: 71
Modeling finite-strain plasticity using physics-informed neural network and assessment of the network performance
Sijun Niu, Enrui Zhang, Yuri Bazilevs, et al.
Journal of the Mechanics and Physics of Solids (2022) Vol. 172, pp. 105177-105177
Open Access | Times Cited: 70
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
Hanxun Jin, Enrui Zhang, Horacio D. Espinosa
Applied Mechanics Reviews (2023) Vol. 75, Iss. 6
Open Access | Times Cited: 66
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: 3
Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives
Mojtaba Mozaffar, Shuheng Liao, Xiaoyu Xie, et al.
Journal of Materials Processing Technology (2021) Vol. 302, pp. 117485-117485
Open Access | Times Cited: 79
Deep learning of free boundary and Stefan problems
Sifan Wang, Paris Perdikaris
Journal of Computational Physics (2020) Vol. 428, pp. 109914-109914
Open Access | Times Cited: 73
Physics-Informed Multifidelity Residual Neural Networks for Hydromechanical Modeling of Granular Soils and Foundation Considering Internal Erosion
Pin Zhang, Zhen‐Yu Yin, Yin‐Fu Jin, et al.
Journal of Engineering Mechanics (2022) Vol. 148, Iss. 4
Closed Access | Times Cited: 52
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: 52
A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network
Shengfeng Xu, Zhenxu Sun, Renfang Huang, et al.
Acta Mechanica Sinica (2022) Vol. 39, Iss. 3
Closed Access | Times Cited: 50
Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning
Hamed Moradi, Akram Al‐Hourani, Gianmarco Concilia, et al.
Biophysical Reviews (2023) Vol. 15, Iss. 1, pp. 19-33
Open Access | Times Cited: 40
Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator–regression neural network
Minglang Yin, Ehsan Ban, Bruno V. Rego, et al.
Journal of The Royal Society Interface (2022) Vol. 19, Iss. 187
Open Access | Times Cited: 39
Power prediction of wind turbine in the wake using hybrid physical process and machine learning models
Huanyu Zhou, Yingning Qiu, Yanhui Feng, et al.
Renewable Energy (2022) Vol. 198, pp. 568-586
Open Access | Times Cited: 38
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: 34
Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images
Lingyue Wang, Ping Hu, Hongwei Zheng, et al.
Geoderma (2023) Vol. 430, pp. 116321-116321
Open Access | Times Cited: 27
Practical uncertainty quantification for space-dependent inverse heat conduction problem via ensemble physics-informed neural networks
Xinchao Jiang, Xin Wang, Ziming Wen, et al.
International Communications in Heat and Mass Transfer (2023) Vol. 147, pp. 106940-106940
Closed Access | Times Cited: 24
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: 24
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