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arXiv · 2509.06067

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

Mianjun Xiao·Peng Song·Yulong Liu·Cedric Korte·Ziyang Xu·Jiale Gao·Jiaqi Lu·Haoyang Nie·Qiantong Deng·Timing Qu
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Abstract

Finite element methods (FEM) for high-temperature superconducting (HTS) magnets become time-consuming at larger scales, restricting the rapid optimization of meter-scale REBCO solenoids. In this work, a surrogate model based on a fully connected residual neural network (FCRN) is developed to predict the current density distribution in REBCO solenoids. Trained on datasets generated from FEM simulations by the T-A formulation, the FCRN model is evaluated under both fast ramping and steady-state scenarios, showing a lower validation loss than the fully connected network (FCN). When extrapolating geometric parameters beyond the training set, the model achieves a relative error of below 10 % for magnetization losses in Case 1 and an average error of 1.2 % for the central magnetic field in Case 2. Furthermore, deploying the steady-state surrogate model for rapid magnet design found the optimal solution within the parameter space under constraints, with a relative central magnetic field error of 0.2 % compared to FEM results. With rapid predictions, this surrogate model offers an efficient tool for the intelligent design of large-scale HTS magnets.

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Mianjun Xiao, Peng Song, Yulong Liu, Cedric Korte, Ziyang Xu, Jiale Gao, Jiaqi Lu, Haoyang Nie, Qiantong Deng, Timing Qu. 2026-03-10. A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network. https://arxiv.org/abs/2509.06067

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