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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:

Neural Network Model for Magnetization Characteristics of Ferromagnetic Materials
Mingxing Tian, Hongchen Li, Huiying Zhang
IEEE Access (2021) Vol. 9, pp. 71236-71243
Open Access | Times Cited: 14

Showing 14 citing articles:

Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics
Diego Serrano, Haoran Li, Shukai Wang, et al.
IEEE Transactions on Power Electronics (2023) Vol. 38, Iss. 11, pp. 14292-14316
Open Access | Times Cited: 33
Magnetic shielding mechanism and structure design of composites at low frequency: A review
Xueping Xu, Wei Liu, Yuejing Huang, et al.
Journal of Magnetism and Magnetic Materials (2023) Vol. 570, pp. 170509-170509
Closed Access | Times Cited: 25
Computing Frequency-Dependent Hysteresis Loops and Dynamic Energy Losses in Soft Magnetic Alloys via Artificial Neural Networks
S. Quondam Antonio, Francesco Riganti Fulginei, Gabriele Maria Lozito, et al.
Mathematics (2022) Vol. 10, Iss. 13, pp. 2346-2346
Open Access | Times Cited: 9
Linearising anhysteretic magnetisation curves: A novel algorithm for finding simulation parameters and magnetic moments
Daniele Carosi, Fabiana Zama, Alessandro Morri, et al.
Mathematics and Computers in Simulation (2024) Vol. 221, pp. 210-221
Open Access | Times Cited: 1
Neural Network Modeling of Complex Hysteresis Loops in Ferromagnetic Materials
Can Ding, Yaolong Bai, Yinbo Ji, et al.
IEEJ Transactions on Electrical and Electronic Engineering (2024)
Closed Access | Times Cited: 1
Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics
Minjie Chen
(2023)
Open Access | Times Cited: 2
Magnetization Mechanism-Inspired Neural Networks for Core Loss Estimation
Qiujie Huang, Yang Li, Jianguo Zhu, et al.
IEEE Transactions on Power Electronics (2024) Vol. 39, Iss. 12, pp. 16382-16390
Closed Access
Quantifying the Complexity of Modeling Power Magnetic Material Characteristics
Minjie Chen
(2022)
Open Access | Times Cited: 3
Deep Artificial Neural Network Method for Magnetic Hysteresis Loop Prediction of Polyvinyl Alcohol@CoFe2O4 Nanocomposites
Sharareh Mirzaee, Kamel Sabahi
Transactions of the Indian Institute of Metals (2024) Vol. 77, Iss. 9, pp. 2651-2657
Closed Access
Parameter Identification of J-A Modified Model of Fe-Based Amorphous Nanocrystalline Magnetic Materials Based on Improved Gray Wolf Algorithm
Zeqing Qu, Shaolei Zhai, Ling Wei, et al.
2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) (2024), pp. 24-28
Closed Access
Numerical analysis of the frequency-dependent Jiles-Atherton hysteresis model using the example of Terfenol-D material
Cheng Zhang, Guangming Xue
AIMS Mathematics (2024) Vol. 9, Iss. 11, pp. 31532-31552
Open Access
An Easily Used Phenomenological Magnetization Model and Its Empirical Expressions Based on Jiles–Atherton Parameters
Guangming Xue, Hongbai Bai, Tuo Li, et al.
Materials (2022) Vol. 15, Iss. 21, pp. 7592-7592
Open Access | Times Cited: 2
On the Complexity of Power Magnetics Modeling
Minjie Chen
(2022)
Open Access | Times Cited: 1
Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics
Minjie Chen
(2022)
Open Access
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