Year: 2025
Type: article
Abstract: ABSTRACT Accurately estimating the magnetization distribution in permanent magnets is critical for optimising their performance in various applications, such as electric motors, generators and magnetic sensors, where precise magnetic field control is essential. A physics‐informed neural network (PINN) is demonstrated to solve the inverse problem of magnetization distribution within the volume of permanent magnets. A neural network is constructed to model the spatially dependent magnetization in the magnet. The physical model, based on the Biot–Savart law, is integrated into the PINN framework. The discrepancy between the magnetic field calculated by the physical model and the externally observed one is used to guide the network training, exhibiting both the model‐based and data‐driven characteristics of the PINN. The accuracy and robustness of the proposed PINN are demonstrated through numerical experiments with both uniform and nonuniform magnetization scenarios, as well as both noise‐free and noisy observation data. This study provides a new approach for solving magnetization distribution estimation problems, benefiting the development of high‐quality permanent magnets for electrical engineering applications.
Source: IET Electric Power Applications
DOI: 10.1049/elp2.70047
Authors: Zhi Gong, Zuqi Tang, Abdelkader Benabou
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,880
Award: G3329305308
OpenAlex ID: W4411893990