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Physics-informed machine learning to approximate the ideal-MHD solution operator in Wendelstein 7-X configurations

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/persons/resource/persons245519

Merlo,  A.       
Stellarator Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society;
Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons203650

Böckenhoff,  D.       
Stellarator Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society;
Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons211443

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

/persons/resource/persons185806

Lazerson,  S. A.       
Stellarator Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society;
Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society;

/persons/resource/persons110123

Pedersen,  T. S.       
Stellarator Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society;

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Merlo, A., Böckenhoff, D., Schilling, J., Lazerson, S. A., Pedersen, T. S., & W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society (2023). Physics-informed machine learning to approximate the ideal-MHD solution operator in Wendelstein 7-X configurations. Talk presented at 86. Jahrestagung der DPG und DPG-Frühjahrstagung der Sektion Materie und Kosmos (SMuK). Dresden. 2023-03-20 - 2023-03-24.


Cite as: https://hdl.handle.net/21.11116/0000-000C-F6C0-5
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