Feasibility of fast predicting input-free parameters of EMC3-EIRENE using 
machine learning

Luo, Y.; Xu, S.; Liang, Y.; Wang, E.; Cai, J.; Feng, Y.; Reiter, D.; Knieps, A.; Brezinsek, S.; Harting, D.; Krychowiak, M.; Dinklage, A.; W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society

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Feasibility of fast predicting input-free parameters of EMC3-EIRENE using machine learning

Luo, Y., Xu, S., Liang, Y., Wang, E., Cai, J., Feng, Y., et al. (2024). Feasibility of fast predicting input-free parameters of EMC3-EIRENE using machine learning. Talk presented at 11th International Workshop on "Stochasticity in Fusion Plasmas (SFP)" – 3D Physics in Stellarators and Tokamaks. Bad Honnef. 2024-04-15 - 2024-04-17.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000F-32F1-8 Versions-Permalink: https://hdl.handle.net/21.11116/0000-000F-32F2-7

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Urheber:
Luo, Y.¹, Autor
Xu, S.¹, Autor
Liang, Y.¹, Autor
Wang, E.¹, Autor
Cai, J.¹, Autor
Feng, Y.², Autor
Reiter, D.¹, Autor
Knieps, A.¹, Autor
Brezinsek, S.¹, Autor
Harting, D.¹, Autor
Krychowiak, M.³, Autor
Dinklage, A.³, Autor
W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society, Autor

Affiliations:
1External Organizations, ou_persistent22
2Stellarator Theory (ST), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856287
3Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society, ou_2040305