Fast inversion of parameters on Jiles-Atherton hysteresis model based on physics-guided deep learning network

She, Saibo, Pang, Xiaochu, Liu, Jun ORCID: https://orcid.org/0000-0002-4549-2833, Zheng, Xinnan, Yu, Kuohai, Zou, Xun, Zhu, Ruoxuan, Guo, Rui and Yin, Wuliang 2025. Fast inversion of parameters on Jiles-Atherton hysteresis model based on physics-guided deep learning network. Engineering Applications of Artificial Intelligence 157 , 111233. 10.1016/j.engappai.2025.111233 Item availability restricted.

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Abstract

The Jiles–Atherton (J–A) hysteresis model is widely used to illustrate the properties of ferromagnetic materials. However, in the inverse J–A hysteresis model, careful selection of the initial seed value is crucial for accurately obtaining the model parameters. This selection impacts both the calculation time and the model’s adaptability. To address this problem, the physics-guided Kolmogorov–Arnold Networks (KAN)-EfficientNet deep learning (DL) model is proposed to achieve fast and accurate J–A model parameters estimation. In the innovative approach, the physics constraints from the inversed J–A hysteresis model and the KAN-Linear layer are utilized to accelerate the convergence of the deep learning model and enhance its accuracy. The theoretical framework of the J–A hysteresis model and its inversed model is illustrated, alongside the development of an experimental platform for data collection to verify the proposed method’s feasibility. Data from classical J–A hysteresis numerical model and simulations are utilized to train the weights of proposed DL model. The compared algorithms are also introduced to illustrate the performance of the proposed model. The experimental data are fed into the analytical J–A hysteresis model and the pre-trained physics-guided KAN-EfficientNet model, respectively. The results show that the proposed method provides high accuracy (error less than 2.41%) in less calculation time (0.3 s) to determine the J–A hysteresis model parameters. The work introduces an innovative physics-guided DL model that significantly enhances the efficiency of J–A hysteresis model parameters estimation, thereby promoting the broader application of J–A hysteresis model in study of ferromagnetic materials.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	0952-1976
Date of First Compliant Deposit:	27 June 2025
Date of Acceptance:	18 May 2025
Last Modified:	14 Jul 2025 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/179364

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