An Optimized Neural Network for Efficient Resource Utilization and Enhanced Accuracy in Magnetic Field Prediction

The article presents a deep learning approach which enables numerical calculation of magnetic fields in various electromagnetic devices. In comparison to the finite element analysis (FEA) method, the trained model demonstrates a significantly faster computation speed. The accuracy of representing information within the solution domain is enhanced through the use of a bitmap technique. A shifted window-based self-attention (SW-MSA) mechanism is employed to analyze device information within the solution domain. Considering the non-negativity property of magnetic flux density, the Softplus activation function is incorporated into the neural network model, resulting in the proposed Softplus-Enhanced Swin-Unet (SESU). Moreover, magnetic field prediction is conducted for three types of electromagnetic devices: coils, transformers, and motors. Compared to commonly used Convolutional Neural Network (CNN) and Vision Transformer (ViT) models, this approach achieves a minimum of 10-fold improvement in prediction accuracy while reducing computational resource consumption by 35%. The proposed method is validated through FEA and comparative experiments.