Physics-augmented machine learning techniques for modeling tokamak dynamics

Abbate, J. A.; Fable, E.; Pankin, A.; Tardini, G.; Kolemen, E.

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Physics-augmented machine learning techniques for modeling tokamak dynamics

Abbate, J. A., Fable, E., Pankin, A., Tardini, G., & Kolemen, E. (2023). Physics-augmented machine learning techniques for modeling tokamak dynamics. Talk presented at 65th Annual Meeting of the APS Division of Plasma Physics. Denver, CO. 2023-10-30 - 2023-11-03.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000E-A9CC-E Versions-Permalink: https://hdl.handle.net/21.11116/0000-000E-A9CD-D

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Urheber:
Abbate, J. A.¹, Autor
Fable, E.², Autor
Pankin, A.¹, Autor
Tardini, G.², Autor
Kolemen, E.¹, Autor

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