Classification of tokamak plasma confinement states with convolutional recurrent neural networks

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Classification of tokamak plasma confinement states with convolutional recurrent neural networks

机译：卷积递归神经网络对托卡马克等离子体约束状态的分类

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During a tokamak discharge, the plasma can vary between different confinement regimes: low (L), high (H) and, in some cases, a temporary (intermediate state), called dithering (D). In addition, while the plasma is in H mode, edge localized modes (ELMs) can occur. The automatic detection of changes between these states, and of ELMs, is important for tokamak operation. Motivated by this, and by recent developments in deep learning, we developed and compared two methods for automatic detection of the occurrence of L-D-H transitions and ELMs, applied on data from the TCV tokamak. These methods consist in a convolutional neural network and a convolutional long short term memory neural network. We measured our results with regards to ELMs using ROC curves and Youden's score index, and regarding state detection using Cohen's Kappa index.

机译：在托卡马克放电期间，血浆可以在不同的限制范围内变化：低（L），高（H）以及在某些情况下称为抖动（D）的临时（中间状态）。此外，当等离子体处于H模式时，会发生边缘定位模式（ELM）。这些状态之间以及ELM之间的变化的自动检测对于托卡马克操作很重要。出于此动机，以及最近在深度学习中的发展，我们开发并比较了两种自动检测L-D-H跃迁和ELM发生的方法，并将它们应用于TCV托卡马克数据。这些方法包括卷积神经网络和卷积长期短期记忆神经网络。我们使用ROC曲线和Youden得分指数来衡量关于ELM的结果，并使用Cohen的Kappa指数来衡量状态检测的结果。

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《Nuclear fusion》 |2020年第3期|036022 (16pp).1-036022 (16pp).16|共16页
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Max Planck Institute for Plasma Physics Boltzmannstraße 2 85748 Garching Germany;

Eindhoven University of Technology 5612 AZ Eindhoven Netherlands;

Ecole Poly technique Federate de Lausanne (EPFL) Swiss Plasma Center (SPC) CH-1015 Lausanne Switzerland;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
原文格式 PDF
正文语种 eng
中图分类
关键词
CNN; LSTM; deep learning; ELM; H mode; L mode; dither;

机译：CNN;LSTM;深度学习榆树;H模式L模式抖动;

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Classification of tokamak plasma confinement states with convolutional recurrent neural networks

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