Towards Applications of Deep Learning Techniques to Establish Surrogate Models 
for the Power Exhaust in Tokamaks

Brenzke, M.; Wiesen, S.; Bernert, M.; Coster, D. P.; Toussaint, U. von; EUROfusion MST1 Team; ASDEX Upgrade Team, Max Planck Institute for Plasma Physics, Max Planck Society

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Conference Paper

Towards Applications of Deep Learning Techniques to Establish Surrogate Models for the Power Exhaust in Tokamaks

MPS-Authors

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Bernert, M.
Plasma Edge and Wall (E2M), Max Planck Institute for Plasma Physics, Max Planck Society;

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Coster, D. P.
Tokamak Theory (TOK), Max Planck Institute for Plasma Physics, Max Planck Society;

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Toussaint, U. von
Numerical Methods in Plasma Physics (NMPP), Max Planck Institute for Plasma Physics, Max Planck Society;

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http://ocs.ciemat.es/EPS2019PAP/pdf/P2.1046.pdf
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Citation

Brenzke, M., Wiesen, S., Bernert, M., Coster, D. P., Toussaint, U. v., EUROfusion MST1 Team, et al. (2019). Towards Applications of Deep Learning Techniques to Establish Surrogate Models for the Power Exhaust in Tokamaks. In C. Riconda, S. Brezinsek, K. McCarty, K. Lancaster, D. Burgess, & P. Brault (Eds.), 46th EPS Conference on Plasma Physics. Geneva: European Physical Society.

Cite as: https://hdl.handle.net/21.11116/0000-0004-D32B-E

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