Modeling of Permanent Magnet Eddy-Current Coupler Based on Unsupervised Physics-Informed Radial-Based Function Neural Networks

Physics-informed neural networks (PINNs) have significant potential for modeling and parameter design in engineering field. While most existing PINNs research focuses on fluid mechanics and thermodynamics, few studies explore its application in electromagnetic field modeling of electromagnetic devices. Modeling the permanent magnet eddy-current coupler (PMECC) to predict its performance characteristics based on geometric parameters and material properties is crucial for its design and optimization. An unsupervised modeling method for PMECC based on physics-informed radial basis neural networks (PIRBFNNs) is presented in this work. The modeling and solving of static magnetic field for devices excited by permanent magnets (PMs) is realized, which solves the problem of the traditional PINN fully connected structure with many parameters and difficult training. We use the magnetic vector potential as the solution objective without providing the magnetic field boundary parameters and without labeling data, which is an unsupervised learning paradigm. The magnetic field distribution and performance of the PMECC can be computed using only the structural parameters. The experimental results show that the proposed PIRBFNN method is basically consistent with the results of the finite element numerical method and the analytical method. Additionally, a transfer learning experimental study was conducted to validate the effectiveness of the network components and training methods proposed in this article. The proposed method can, furthermore, be applied to the modeling and analysis of various devices using PM excitations.