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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:

Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions
Majid Rasht‐Behesht, Christian Huber, Khemraj Shukla, et al.
Journal of Geophysical Research Solid Earth (2022) Vol. 127, Iss. 5
Open Access | Times Cited: 196

Showing 1-25 of 196 citing articles:

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
Sensing prior constraints in deep neural networks for solving exploration geophysical problems
Xinming Wu, Jianwei Ma, Xu Si, et al.
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 23
Open Access | Times Cited: 44
Physics‐Informed Neural Networks of the Saint‐Venant Equations for Downscaling a Large‐Scale River Model
Dongyu Feng, Zeli Tan, Qizhi He
Water Resources Research (2023) Vol. 59, Iss. 2
Open Access | Times Cited: 42
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
Recent advances in earthquake seismology using machine learning
Hisahiko Kubo, Makoto Naoi, Masayuki Kano
Earth Planets and Space (2024) Vol. 76, Iss. 1
Open Access | Times Cited: 20
Robust Variational Physics-Informed Neural Networks
Sergio Rojas, Paweł Maczuga, Judit Muñoz‐Matute, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 425, pp. 116904-116904
Open Access | Times Cited: 15
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
Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis
Zaharaddeen Karami Lawal, Hayati Yassin, Daphne Teck Ching Lai, et al.
Big Data and Cognitive Computing (2022) Vol. 6, Iss. 4, pp. 140-140
Open Access | Times Cited: 66
Applications of deep neural networks in exploration seismology: A technical survey
S. Mostafa Mousavi, Gregory C. Beroza, Tapan Mukerji, et al.
Geophysics (2023) Vol. 89, Iss. 1, pp. WA95-WA115
Closed Access | Times Cited: 37
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
Pu Ren, Chengping Rao, Su Chen, et al.
Computer Physics Communications (2023) Vol. 295, pp. 109010-109010
Open Access | Times Cited: 37
FWIGAN: Full‐Waveform Inversion via a Physics‐Informed Generative Adversarial Network
Fangshu Yang, Jianwei Ma
Journal of Geophysical Research Solid Earth (2023) Vol. 128, Iss. 4
Closed Access | Times Cited: 34
Seismic Inversion Based on Acoustic Wave Equations Using Physics-Informed Neural Network
Yijie Zhang, Xueyu Zhu, Jinghuai Gao
IEEE Transactions on Geoscience and Remote Sensing (2023) Vol. 61, pp. 1-11
Closed Access | Times Cited: 31
Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model
Mingliang Liu, Divakar Vashisth, Darío Graña, et al.
Journal of Geophysical Research Solid Earth (2023) Vol. 128, Iss. 3
Closed Access | Times Cited: 29
Physics-Informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media
Salah A. Faroughi, Ramin Soltanmohammadi, Pingki Datta, et al.
Mathematics (2023) Vol. 12, Iss. 1, pp. 63-63
Open Access | Times Cited: 29
Self-adaptive physics-driven deep learning for seismic wave modeling in complex topography
Yi Ding, Su Chen, Xiaojun Li, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106425-106425
Closed Access | Times Cited: 28
Physics-Guided Data-Driven Seismic Inversion: Recent progress and future opportunities in full-waveform inversion
Youzuo Lin, James Theiler, Brendt Wohlberg
IEEE Signal Processing Magazine (2023) Vol. 40, Iss. 1, pp. 115-133
Closed Access | Times Cited: 26
Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?
Yunyue Elita Li, Daniel O’Malley, Greg Beroza, et al.
Journal of Geophysical Research Solid Earth (2023) Vol. 128, Iss. 1
Closed Access | Times Cited: 24
Solving Seismic Wave Equations on Variable Velocity Models With Fourier Neural Operator
Bian Li, Hanchen Wang, Shihang Feng, et al.
IEEE Transactions on Geoscience and Remote Sensing (2023) Vol. 61, pp. 1-18
Open Access | Times Cited: 23
Rapid Seismic Waveform Modeling and Inversion With Neural Operators
Yan Yang, Angela F. Gao, Kamyar Azizzadenesheli, et al.
IEEE Transactions on Geoscience and Remote Sensing (2023) Vol. 61, pp. 1-12
Open Access | Times Cited: 22
Room impulse response reconstruction with physics-informed deep learning
Xenofon Karakonstantis, Diego Caviedes-Nozal, Antoine Richard, et al.
The Journal of the Acoustical Society of America (2024) Vol. 155, Iss. 2, pp. 1048-1059
Open Access | Times Cited: 11
3D elastic wave propagation with a Factorized Fourier Neural Operator (F-FNO)
Fanny Lehmann, Filippo Gatti, Michaël Bertin, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 420, pp. 116718-116718
Open Access | Times Cited: 10
When geoscience meets generative AI and large language models: Foundations, trends, and future challenges
Abdenour Hadid, Tanujit Chakraborty, D. Busby
Expert Systems (2024) Vol. 41, Iss. 10
Closed Access | Times Cited: 9
Simulating seismic multifrequency wavefields with the Fourier feature physics-informed neural network
Chao Song, Yanghua Wang
Geophysical Journal International (2022) Vol. 232, Iss. 3, pp. 1503-1514
Open Access | Times Cited: 35
Physics-informed deep learning approach for modeling crustal deformation
Tomohisa Okazaki, Takeo Ito, Kazuro Hirahara, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 29
Wave Equation Modeling via Physics-Informed Neural Networks: Models of Soft and Hard Constraints for Initial and Boundary Conditions
Shaikhah Alkhadhr, Mohamed Almekkawy
Sensors (2023) Vol. 23, Iss. 5, pp. 2792-2792
Open Access | Times Cited: 17
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