Implementing deep learning-based disruption prediction in a drifting data environment of new tokamak: HL-3

Implementing deep learning-based disruption prediction in a drifting data environment of new tokamak: HL-3

A deep learning-based disruption prediction algorithm has been implemented on a new tokamak, HL-3. An Area Under receiver-operator characteristic Curve of 0.940 has been realized offline over a test campaign involving 72 disruptive and 240 non-disruptive shots, despite the limited training data avai...

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Bibliographic Details
Main Authors: Zongyu Yang, Wulyu Zhong, Fan Xia, Zhe Gao, Xiaobo Zhu, Jiyuan Li, Liwen Hu, Zhaohe Xu, Da Li, Guohui Zheng, Yihang Chen, Junzhao Zhang, Bo Li, Xiaolong Zhang, Yiren Zhu, Ruihai Tong, Yunbo Dong, Yipo Zhang, Boda Yuan, Xin Yu, Zongyuhui He, Wenjing Tian, Xinwen Gong, Min Xu
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Nuclear Fusion
Subjects:
disruption prediction
deep learning
HL-3
tokamak
Online Access:https://doi.org/10.1088/1741-4326/ada396
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