Dimensionless Physics-Informed Neural Network for Electromagnetic Field Modelling of Permanent Magnet Eddy Current Coupler

Abstract

To design the permanent magnetic eddy current couplers (PMECCs), modelling the magnetic field is essential. Traditional equivalent magnetic circuit methods and analytical methods often rely heavily on expert experience, whereas finite element methods (FEM) demand significant computational resources and time. Recently, the physics-informed neural network (PINN) has emerged as a novel approach for modelling electromagnetic fields. To fully harness the potential of PINN, eliminate reliance on data sets, and enhance the generalisation ability of multi-scale physical systems, we simplify the physical model of PMECCs and analyse its inherent boundary conditions based on the fundamental properties of electromagnetic fields. A dimensionless and unsupervised PINN, employing dimensional analysis to reduce the dimensions of the physical variables in the system was proposed. The dimensionless PINN (DPINN) is trained through unsupervised learning to solve the magnetic field equations and predict PMECC performance. Furthermore, dimensional analysis and transfer learning method are applied to enable the network to address a broader class of problems, resulting in a 92% reduction in training cost. The solution results, compared with those from the finite element method and analytical solution, exhibit similar error magnitudes (10⁻⁴ Wb/m), confirming the method's high accuracy.