A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network

Abstract

Finite element method (FEM) is widely used in high-temperature superconducting (HTS) magnets, but its computational cost increases with magnet size and becomes time-consuming for meter-scale magnets, especially when multi-physics couplings are considered, which limits the fast design of large-scale REBCO magnet systems. In this work, a surrogate model based on a fully connected residual neural network (FCRN) is developed to predict the space-time current density distribution in REBCO solenoids. Training datasets were generated from FEM simulations with varying numbers of turns and pancakes. The results demonstrate that, for deeper networks, the FCRN architecture achieves better convergence than conventional fully connected network (FCN), with the configuration of 12 residual blocks and 256 neurons per layer providing the most favorable balance between training accuracy and generalization capability. Extrapolation studies show that the model can reliably predict magnetization losses for up to 50% beyond the training range, with maximum errors below 10%. The surrogate model achieves predictions several orders of magnitude faster than FEM and still remains advantageous when training costs are included. These results indicate that the proposed FCRN-based surrogate model provides both accuracy and efficiency, offering a promising tool for the rapid analysis of large-scale HTS magnets.