Machine learning approach for operational phases identification in H-mode 
density limit disruptions

Lacquaniti, M.; Sias, G.; Cannas, B.; Fanni, A.; Maraschek, M.; Gude, A.; EUROfusion MST1 Team

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Machine learning approach for operational phases identification in H-mode density limit disruptions

Lacquaniti, M., Sias, G., Cannas, B., Fanni, A., Maraschek, M., Gude, A., et al. (2021). Machine learning approach for operational phases identification in H-mode density limit disruptions. Poster presented at 47th EPS Conference on Plasma Physics, Virtual.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0008-C7D6-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0008-C7D7-5

Genre: Poster

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Creators:
Lacquaniti, M.¹, Author
Sias, G.¹, Author
Cannas, B.¹, Author
Fanni, A.¹, Author
Maraschek, M.², Author
Gude, A.², Author
EUROfusion MST1 Team, Author

Affiliations:
1External Organizations, ou_persistent22
2Tokamak Scenario Development (E1), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856321

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Language(s): eng - English

Dates: Created: 2021

Publication Status: Not specified

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Title: 47th EPS Conference on Plasma Physics

Place of Event: Virtual

Start-/End Date: 2021-06-21 - 2021-06-25

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