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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Conference Paper

Machine learning approach for operational phases identification in H-mode Density Limit disruptions

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Maraschek, M.
Tokamak Scenario Development (E1), Max Planck Institute for Plasma Physics, Max Planck Society;

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Gude, A.
Tokamak Scenario Development (E1), Max Planck Institute for Plasma Physics, Max Planck Society;

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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. In G. Giruzzi, C. Arnas, D. Borba, A. Gopla, S. Lebedev, & M. Mantsinen (Eds.), 47th EPS Conference on Plasma Physics. Geneva: European Physical Society.

Cite as: https://hdl.handle.net/21.11116/0000-0009-A8EF-D

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