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  GS‑DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad–Shafranov equation

Joung, S., Ghim, Y., Kim, J., Kwak, S., Kwon, D., Sung, C., et al. (2023). GS‑DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad–Shafranov equation. Scientific Reports, 13: 15799. doi:10.1038/s41598-023-42991-5.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-EAAF-7 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-EAB0-4
Genre: Journal Article

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joung_gs-deepnet.pdf (Supplementary material), 3MB
 
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https://doi.org/10.1038/s41598-023-42991-5 (Publisher version)
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 Creators:
Joung, S.1, Author
Ghim, Y.‑C.1, Author
Kim, J.1, Author
Kwak, S.2, Author                 
Kwon, D.1, Author
Sung, C.1, Author
Kim, D.1, Author
Kim, H.‑S.1, Author
Bak, J. G.1, Author
Yoon, S. W.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Stellarator Dynamics and Transport (E5), Max Planck Institute for Plasma Physics, Max Planck Society, ou_2040306              

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Language(s): eng - English
 Dates: 2023
 Publication Status: Published online
 Pages: 15 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41598-023-42991-5
 Degree: -

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Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: - Volume / Issue: 13 Sequence Number: 15799 Start / End Page: - Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322