Deep Learning for the Analysis of Disruption Precursors Based on Plasma Tomography-Reference-Cited by-同舟云学术

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Deep Learning for the Analysis of Disruption Precursors Based on Plasma Tomography

Published:2020-11-04 Issue:8 Volume:76 Page:901-911
ISSN:1536-1055
Container-title:Fusion Science and Technology
language:en
Short-container-title:Fusion Science and Technology

Author:

Ferreira Diogo R.¹²^ORCID,Carvalho Pedro J.²³,Sozzi Carlo⁴,Lomas Peter J.³,JET Contributors

Affiliation:

1. EUROfusion Consortium, JET Culham Science Centre, Abingdon OX14 3DB, United Kingdom

2. Universidade de Lisboa, Instituto de Plasmas e Fusão Nuclear (IPFN) Instituto Superior Técnico (IST), Lisboa 1049-001, Portugal

3. United Kingdom Atomic Energy Authority (UKAEA), Culham Centre for Fusion Energy (CCFE), Abingdon OX14 3DB, United Kingdom

4. Istituto per la Scienza e Tecnologia Dei Plasmi (ISTP), Consiglio Nazionale Delle Ricerche (CNR), Milano 20125, Italy

Publisher

Informa UK Limited

Subject

Mechanical Engineering,General Materials Science,Nuclear Energy and Engineering,Nuclear and High Energy Physics,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15361055.2020.1820749

Reference28 articles.

1. Scenario development for D–T operation at JET

2. Risk Mitigation for ITER by a Prolonged and Joint International Operation of JET

3. Survey of disruption causes at JET

4. Plasma operation with an all metal first-wall: Comparison of an ITER-like wall with a carbon wall in JET

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1. Enhancing disruption prediction through Bayesian neural network in KSTAR;Plasma Physics and Controlled Fusion;2024-05-16

2. Static performance prediction of long-pulse negative ion based neutral beam injection experiment;Plasma Physics and Controlled Fusion;2024-04-23

3. Disruption prediction and analysis through multimodal deep learning in KSTAR;Fusion Engineering and Design;2024-03

4. A systematic investigation of radiation collapse for disruption avoidance and prevention on JET tokamak;Matter and Radiation at Extremes;2023-06-13

5. MHD mode tracking using high-speed cameras and deep learning;Plasma Physics and Controlled Fusion;2023-05-30

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