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

Joung, S.; Ghim, Y.‑C.; Kim, J.; Kwak, S.; Kwon, D.; Sung, C.; Kim, D.; Kim, H.‑S.; Bak, J. G.; Yoon, S. W.

doi:10.1038/s41598-023-42991-5

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

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

MPS-Authors

/persons/resource/persons203629

Kwak, S.
Stellarator Dynamics and Transport (E5), Max Planck Institute for Plasma Physics, Max Planck Society;

External Resource

https://doi.org/10.1038/s41598-023-42991-5
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-000D-EAAF-7

Abstract

There is no abstract available