AI RESEARCH
A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network
arXiv CS.LG
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ArXi:2509.06067v2 Announce Type: replace 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.