Adaptive anomaly detection disruption prediction starting from first discharge on tokamak

Adaptive anomaly detection disruption prediction starting from first discharge on tokamak

Plasma disruption presents a significant challenge in tokamak fusion, especially in large-size devices like ITER, where it causes severe damage. While current data-driven machine learning methods perform well in disruption prediction, they require extensive discharge data for model training. However...

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Bibliographic Details
Main Authors: X.K. Ai, W. Zheng, M. Zhang, Y.H. Ding, D.L. Chen, Z.Y. Chen, B.H. Guo, C.S. Shen, N.C. Wang, Z.J. Yang, Z.P. Chen, Y. Pan, B. Shen, B.J. Xiao, J-TEXT team
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Nuclear Fusion
Subjects:
tokamak
disruption prediction
deep learning
adaptive learning
anomaly detection
Online Access:https://doi.org/10.1088/1741-4326/ada9a9
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https://doi.org/10.1088/1741-4326/ada9a9

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