Overview of machine learning applications in fusion plasma experiments on J-TEXT tokamak

Wei ZHENG; Fengming XUE; Chengshuo SHEN; Yu ZHONG; Xinkun AI; Zhongyong CHEN; Yonghua DING; Ming ZHANG; Zhoujun YANG; Nengchao WANG; Zhichao ZHANG; Jiaolong DONG; Chouyao TANG; Yuan PAN

doi:10.1088/2058-6272/ac9e46

Plasma Science and Technology > 2022 > 24(12): 124003. > DOI: 10.1088/2058-6272/ac9e46

Wei ZHENG, Fengming XUE, Chengshuo SHEN, Yu ZHONG, Xinkun AI, Zhongyong CHEN, Yonghua DING, Ming ZHANG, Zhoujun YANG, Nengchao WANG, Zhichao ZHANG, Jiaolong DONG, Chouyao TANG, Yuan PAN. Overview of machine learning applications in fusion plasma experiments on J-TEXT tokamak[J]. Plasma Science and Technology, 2022, 24(12): 124003. DOI: 10.1088/2058-6272/ac9e46

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Overview of machine learning applications in fusion plasma experiments on J-TEXT tokamak

1.
International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics, State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
2.
Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China

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Author Bio:
Wei ZHENG, E-mail: zhengwei@hust.edu.cn
Received Date: August 30, 2022
Revised Date: September 29, 2022
Accepted Date: October 26, 2022
Available Online: December 05, 2023
Published Date: November 29, 2022

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Abstract

Abstract

Machine learning research and applications in fusion plasma experiments are one of the main subjects on J-TEXT. Since 2013, various kinds of traditional machine learning, as well as deep learning methods have been applied to fusion plasma experiments. Further applications in the real-time experimental environment have proved the feasibility and effectiveness of the methods. For disruption prediction, we started by predicting disruptions of limited classes with a short warning time that could not meet the requirements of the mitigation system. After years of study, nowadays disruption prediction methods on J-TEXT are able to predict all kinds of disruptions with a high success rate and long enough warning time. Furthermore, cross-device disruption prediction methods have obtained promising results. Interpretable analysis of the models are studied. For diagnostics data processing, efforts have been made to reduce manual work in processing and to increase the robustness of the diagnostic system. Models based on both traditional machine learning and deep learning have been applied to real-time experimental environments. The models have been cooperating with the plasma control system and other systems, to make joint decisions to further support the experiments.
- machine learning,
- disruption prediction,
- diagnostics data processing,
- J-TEXT

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Acknowledgments

The authors are very grateful for the help of J-TEXT team. This work is supported by the National Key R & D Program of China (No. 2022YFE03040004) and National Natural Science Foundation of China (No. 51821005).

References (33)

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