Detection and prediction of a beam-driven mode in field-reversed configuration plasma with recurrent neural networks

被引：4

作者：

Scott, Cory ^{[1
]}

Dettrick, Sean ^{[2
]}

Tajima, Toshiki ^{[2
,3
]}

Magee, Richard ^{[2
]}

Mjolsness, Eric ^{[1
]}

机构：

[1] Univ Calif Irvine, Dept Comp Sci, Irvine, CA 92697 USA

[2] TAE Technol Inc, Foothill Ranch, CA USA

[3] Univ Calif Irvine, Dept Phys, Irvine, CA 92717 USA

来源：

NUCLEAR FUSION | 2020年 / 60卷 / 12期

关键词：

field-reversed configuration; fusion; plasma physics; machine learning; convolutional neural networks; recurrent neural networks; DISRUPTIONS;

D O I：

10.1088/1741-4326/abb328

中图分类号：

O35 [流体力学]; O53 [等离子体物理学];

学科分类号：

070204 ; 080103 ; 080704 ;

摘要：

Energetic beams excite semi-repetitive modes ('staircase mode') in the field-reversed configuration (FRC) plasma. We explore several neural network architectures to detect, and in some cases predict, this type of mode onset. We weigh the performance of these architectures and find that recurrent neural networks (RNNs), specifically long short-term memory (LSTM) networks, outperform all other models we examine. LSTMs can predict the onset of staircase with a lead window of 0.2 ms, which has implications for plasma longevity and is a promising direction for similar analysis in FRC devices in the future.

引用

页数：11

共 50 条

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