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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 26-50 of 143 citing articles:

A physics-informed GAN framework based on model-free data-driven computational mechanics
Kerem Ciftci, Klaus Hackl
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 424, pp. 116907-116907
Open Access | Times Cited: 8
Deep learning for inverse material characterization
Yousef Ghaffari Motlagh, Farshid Fathi, John C. Brigham, et al.
Computer Methods in Applied Mechanics and Engineering (2025) Vol. 436, pp. 117650-117650
Closed Access | Times Cited: 1
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
Enrui Zhang, Minglang Yin, George Em Karniadakis
arXiv (Cornell University) (2020)
Open Access | Times Cited: 59
Physics‐constrained hierarchical data‐driven modelling framework for complex path‐dependent behaviour of soils
Pin Zhang, Zhen‐Yu Yin, Yin‐Fu Jin, et al.
International Journal for Numerical and Analytical Methods in Geomechanics (2022) Vol. 46, Iss. 10, pp. 1831-1850
Closed Access | Times Cited: 37
A physics-informed data-driven approach for consolidation analysis
Pin Zhang, Zhen‐Yu Yin, Brian Sheil
Géotechnique (2022) Vol. 74, Iss. 7, pp. 620-631
Closed Access | Times Cited: 36
Scalable algorithms for physics-informed neural and graph networks
Khemraj Shukla, Mengjia Xu, Nathaniel Trask, et al.
Data-Centric Engineering (2022) Vol. 3
Open Access | Times Cited: 32
CENN: Conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries
Yizheng Wang, Jia Sun, Wei Li, et al.
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 400, pp. 115491-115491
Open Access | Times Cited: 31
Investigating molecular transport in the human brain from MRI with physics-informed neural networks
Bastian Zapf, Johannes Haubner, Miroslav Kuchta, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 30
Deep Learning for Computational Hemodynamics: A Brief Review of Recent Advances
Amirtahà Taebi
Fluids (2022) Vol. 7, Iss. 6, pp. 197-197
Open Access | Times Cited: 29
Surrogate modeling of parameterized multi-dimensional premixed combustion with physics-informed neural networks for rapid exploration of design space
Kai Liu, Kun Luo, Yuzhou Cheng, et al.
Combustion and Flame (2023) Vol. 258, pp. 113094-113094
Closed Access | Times Cited: 20
Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke
Ahmed Qureshi, Gregory Y.H. Lip, David Nordsletten, et al.
Frontiers in Cardiovascular Medicine (2023) Vol. 9
Open Access | Times Cited: 19
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: 19
Machine Learning and the Conundrum of Stroke Risk Prediction
Yaacoub Chahine, Matthew J Magoon, Bahetihazi Maidu, et al.
Arrhythmia & Electrophysiology Review (2023) Vol. 12
Open Access | Times Cited: 18
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: 16
On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures
Shirko Faroughi, A. Darvishi, Shahed Rezaei
Acta Mechanica (2023) Vol. 234, Iss. 11, pp. 5673-5695
Closed Access | Times Cited: 16
ReF-nets: Physics-informed neural network for Reynolds equation of gas bearing
Liangliang Li, Yunzhu Li, Qiuwan Du, et al.
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 391, pp. 114524-114524
Closed Access | Times Cited: 26
A generative modeling framework for inferring families of biomechanical constitutive laws in data-sparse regimes
Minglang Yin, Zongren Zou, Enrui Zhang, et al.
Journal of the Mechanics and Physics of Solids (2023) Vol. 181, pp. 105424-105424
Open Access | Times Cited: 15
Accelerated simulation methodologies for computational vascular flow modelling
Michael MacRaild, Ali Sarrami‐Foroushani, Toni Lassila, et al.
Journal of The Royal Society Interface (2024) Vol. 21, Iss. 211
Open Access | Times Cited: 5
Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
Siyuan Song, Hanxun Jin
Soft Matter (2024) Vol. 20, Iss. 30, pp. 5915-5926
Open Access | Times Cited: 5
DCEM: A deep complementary energy method for linear elasticity
Yizheng Wang, Jia Sun, Timon Rabczuk, et al.
International Journal for Numerical Methods in Engineering (2024)
Closed Access | Times Cited: 5
An enhanced hybrid adaptive physics-informed neural network for forward and inverse PDE problems
Kuang Luo, Shaolin Liao, Zhong Guan, et al.
Applied Intelligence (2025) Vol. 55, Iss. 4
Closed Access
ICPINN: Integral conservation physics-informed neural networks based on adaptive activation functions for 3D blood flow simulations
Youqiong Liu, Li Cai, Yaping Chen, et al.
Computer Physics Communications (2025) Vol. 311, pp. 109569-109569
Closed Access
Research on intelligent prediction method of supersonic flow field in scramjet based on deep learning: A review
Xue Deng, Ye Tian, Erda Chen, et al.
Expert Systems with Applications (2025), pp. 127500-127500
Closed Access
Mesh-Free Surrogate Models for Structural Mechanic FEM Simulation: A Comparative Study of Approaches
Johannes G. Hoffer, Bernhard C. Geiger, Patrick Ofner, et al.
Applied Sciences (2021) Vol. 11, Iss. 20, pp. 9411-9411
Open Access | Times Cited: 30
Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning
Richa R. Naik, Armi Tiihonen, Janak Thapa, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 19

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