A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917326687830016 |
|---|---|
| author | Xiao, Mianjun Song, Peng Liu, Yulong Korte, Cedric Xu, Ziyang Gao, Jiale Lu, Jiaqi Nie, Haoyang Deng, Qiantong Qu, Timing |
| author_facet | Xiao, Mianjun Song, Peng Liu, Yulong Korte, Cedric Xu, Ziyang Gao, Jiale Lu, Jiaqi Nie, Haoyang Deng, Qiantong Qu, Timing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06067 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network Xiao, Mianjun Song, Peng Liu, Yulong Korte, Cedric Xu, Ziyang Gao, Jiale Lu, Jiaqi Nie, Haoyang Deng, Qiantong Qu, Timing Machine Learning 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. |
| title | A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.06067 |