Machine learning methods for probabilistic locked-mode predictors in tokamak plasmas-Reference-Cited by-同舟云学术

Machine learning methods for probabilistic locked-mode predictors in tokamak plasmas

Published:2021-08 Issue:8 Volume:28 Page:082106
ISSN:1070-664X
Container-title:Physics of Plasmas
language:en
Short-container-title:Physics of Plasmas

Author:

Akçay Cihan¹^ORCID,Finn John M.²^ORCID,Brennan Dylan P.³^ORCID,Burr Thomas⁴,Kürkçüoğlu Doğa M.⁵^ORCID

Affiliation:

1. General Atomics, 3550 General Atomics Drive, San Diego, California 92121, USA

2. Tibbar Plasma Technologies, Incorporated, 274 DP Rd., Los Alamos, New Mexico 87544, USA

3. Princeton University, Princeton, New Jersey 08544, USA

4. Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA

5. Fermilab, Batavia, Illinois 60510-5011, USA

Funder

Fusion Energy Sciences

Publisher

AIP Publishing

Subject

Condensed Matter Physics

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0053670

Reference57 articles.

1. The interaction of resonant magnetic perturbations with rotating plasmas

2. Interaction of tearing modes with external structures in cylindrical geometry (plasma)

3. Mode-locking is not always the primary cause of disruptions in a tokamak. EAST disruptions, for example, are primarily caused by vertical displacement events,12 while mode-locking due to NTM's was the largest single cause of disruptions in JET with the carbon wall.

4. The influence of rotation on the βN threshold for the 2∕1 neoclassical tearing mode in DIII-D

5. Predictive simulations of ITER including neutral beam driven toroidal rotation

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1. Disruption prediction with artificial intelligence techniques in tokamak plasmas;Nature Physics;2022-06-06