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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 for an inverse problem in peridynamic models
Fabio V. Difonzo, L. Lopez, Sabrina Francesca Pellegrino
Engineering With Computers (2024)
Open Access | Times Cited: 10

Showing 10 citing articles:

Physics informed neural networks for learning the horizon size in bond-based peridynamic models
Fabio V. Difonzo, L. Lopez, Sabrina Francesca Pellegrino
Computer Methods in Applied Mechanics and Engineering (2025) Vol. 436, pp. 117727-117727
Open Access | Times Cited: 2
Boundary integrated neural networks for 2D elastostatic and piezoelectric problems
Peijun Zhang, Longtao Xie, Yan Gu, et al.
International Journal of Mechanical Sciences (2024) Vol. 280, pp. 109525-109525
Open Access | Times Cited: 12
On the use of exponential basis functions in the PINN architecture: An enhanced solution approach for the Laplace, Helmholtz, and elasto-static equations
Sadegh Ghamsari Esfahani, B. Movahedian, Saeid Sarrami-Foroushani, et al.
Computers & Mathematics with Applications (2025) Vol. 183, pp. 234-255
Closed Access | Times Cited: 1
PD-KINN: Kolmogorov–Arnold representation theorem enhanced peridynamic-informed neural network for predicting elastic deformation and brittle damage
Yongquan Nie, Ying Zhang, Yan Zhu, et al.
Engineering Analysis with Boundary Elements (2025) Vol. 176, pp. 106214-106214
Closed Access
Determining Longitudinal Dispersion Coefficients in Micro-Fractured Porous Media Via an Inverse Physics-Informed Neural Network
Muyuan Wang, Keliu Wu, Qingyuan Zhu, et al.
(2025)
Closed Access
Inverse Physics-Informed Neural Networks for transport models in porous materials
Marco Berardi, Fabio V. Difonzo, Matteo Icardi
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 435, pp. 117628-117628
Open Access | Times Cited: 3
An inverse problem of determining the parameters in diffusion equations by using fractional physics-informed neural networks
M. Srati, A. Oulmelk, Lekbir Afraites, et al.
Applied Numerical Mathematics (2024)
Closed Access | Times Cited: 1
Step-by-step time discrete Physics-Informed Neural Networks with application to a sustainability PDE model
Carmine Valentino, Giovanni Pagano, Dajana Conte, et al.
Mathematics and Computers in Simulation (2024)
Open Access | Times Cited: 1
Investigating neural networks with groundwater flow equation loss
Vincenzo Schiano Di Cola, Vittorio Bauduin, Marco Berardi, et al.
Mathematics and Computers in Simulation (2024) Vol. 230, pp. 80-93
Closed Access
Impact of collocation point sampling techniques on PINN performance in groundwater flow predictions
Vittorio Bauduin, Salvatore Cuomo, Vincenzo Schiano Di Cola
Journal of Computational Mathematics and Data Science (2024), pp. 100107-100107
Open Access
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