Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data-Reference-Cited by-同舟云学术

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

Published:2020-06 Issue:6 Volume:27 Page:062510
ISSN:1070-664X
Container-title:Physics of Plasmas
language:en
Short-container-title:Physics of Plasmas

Author:

Churchill R. M.¹^ORCID,Tobias B.²,Zhu Y.³^ORCID,

Affiliation:

1. Princeton Plasma Physics Laboratory, 100 Stellarator Road, Princeton, New Jersey 08540, USA

2. Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

3. University of California at Davis, Davis, California 95616, USA

Funder

U.S. Department of Energy

Publisher

AIP Publishing

Subject

Condensed Matter Physics

Link

http://aip.scitation.org/doi/am-pdf/10.1063/1.5144458

Reference31 articles.

1. Deep learning

2. 2D/3D electron temperature fluctuations near explosive MHD instabilities accompanied by minor and major disruptions

3. Chapter 3: MHD stability, operational limits and disruptions

4. Results of the JET real-time disruption predictor in the ITER-like wall campaigns

5. Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DIII-D

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