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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:

Extraction of mechanical properties of materials through deep learning from instrumented indentation
Lu Lu, Ming Dao, Punit Kumar, et al.
Proceedings of the National Academy of Sciences (2020) Vol. 117, Iss. 13, pp. 7052-7062
Open Access | Times Cited: 264

Showing 1-25 of 264 citing articles:

Physics-informed machine learning
George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, et al.
Nature Reviews Physics (2021) Vol. 3, Iss. 6, pp. 422-440
Closed Access | Times Cited: 3358
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: 1017
Artificial intelligence and machine learning in design of mechanical materials
Kai Guo, Zhenze Yang, Chi‐Hua Yu, et al.
Materials Horizons (2020) Vol. 8, Iss. 4, pp. 1153-1172
Open Access | Times Cited: 420
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
Sifan Wang, Hanwen Wang, Paris Perdikaris
Science Advances (2021) Vol. 7, Iss. 40
Open Access | Times Cited: 346
Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
Qiming Zhu, Zeliang Liu, Jinhui Yan
Computational Mechanics (2021) Vol. 67, Iss. 2, pp. 619-635
Open Access | Times Cited: 306
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
Lu Lu, Xuhui Meng, Shengze Cai, et al.
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 393, pp. 114778-114778
Open Access | Times Cited: 267
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: 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: 144
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: 141
Recent Advances in Machine Learning for Fiber Optic Sensor Applications
Abhishek Venketeswaran, Nageswara Lalam, Jeffrey Wuenschell, et al.
Advanced Intelligent Systems (2021) Vol. 4, Iss. 1
Closed Access | Times Cited: 140
Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios
Xu Chen, Ba Trung Cao, Yong Yuan, et al.
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 405, pp. 115852-115852
Open Access | Times Cited: 113
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Lu Lu, Raphaël Pestourie, Steven G. Johnson, et al.
Physical Review Research (2022) Vol. 4, Iss. 2
Open Access | Times Cited: 91
Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
Kedar Hippalgaonkar, Qianxiao Li, Xiaonan Wang, et al.
Nature Reviews Materials (2023) Vol. 8, Iss. 4, pp. 241-260
Closed Access | Times Cited: 84
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: 65
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Min Zhu, Handi Zhang, Anran Jiao, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 412, pp. 116064-116064
Open Access | Times Cited: 56
Deep learning in computational mechanics: a review
Leon Herrmann, Stefan Kollmannsberger
Computational Mechanics (2024) Vol. 74, Iss. 2, pp. 281-331
Open Access | Times Cited: 27
Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications
Haoteng Hu, Lehua Qi, Xujiang Chao
Thin-Walled Structures (2024) Vol. 205, pp. 112495-112495
Closed Access | Times Cited: 19
Review of uniqueness challenge in inverse analysis of nanoindentation
Xu Long, Yaxi Li, Ziyi Shen, et al.
Journal of Manufacturing Processes (2024) Vol. 131, pp. 1897-1916
Closed Access | Times Cited: 15
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: 86
Machine learning for material characterization with an application for predicting mechanical properties
Anke Stoll, Peter Benner
GAMM-Mitteilungen (2021) Vol. 44, Iss. 1
Open Access | Times Cited: 76
Deep learning for material synthesis and manufacturing systems: A review
V. Bhuvaneswari, M. Priyadharshini, Chokkalingam Deepa, et al.
Materials Today Proceedings (2021) Vol. 46, pp. 3263-3269
Closed Access | Times Cited: 70
Data-driven tissue mechanics with polyconvex neural ordinary differential equations
Vahidullah Taç, Francisco Sahli Costabal, Adrián Buganza Tepole
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 398, pp. 115248-115248
Open Access | Times Cited: 66
Learning hidden elasticity with deep neural networks
Chun‐Teh Chen, Grace X. Gu
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 31
Open Access | Times Cited: 60
Profilometry‐Based Inverse Finite Element Method Indentation Plastometry
T.W. Clyne, J. E. Campbell, Max Burley, et al.
Advanced Engineering Materials (2021) Vol. 23, Iss. 9
Open Access | Times Cited: 59
Long-time integration of parametric evolution equations with physics-informed DeepONets
Sifan Wang, Paris Perdikaris
Journal of Computational Physics (2022) Vol. 475, pp. 111855-111855
Open Access | Times Cited: 58
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