Physics-Informed Neural Network for Magnetization Distribution Estimation

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.