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Deep Learning-Based Real Time Electron Density Profile Reconstruction for Control at ASDEX Upgrade

MPS-Authors
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Illerhaus,  J.       
Tokamak Scenario Development (E1), Max Planck Institute for Plasma Physics, Max Planck Society;
External Organizations;

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

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

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

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Fischer,  R.       
Physics of the Plasma Edge (E2), Max Planck Institute for Plasma Physics, Max Planck Society;

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Kudlacek,  O.       
Tokamak Scenario Development (E1), 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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Zohm,  H.       
Tokamak Scenario Development (E1), Max Planck Institute for Plasma Physics, Max Planck Society;

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Jenko,  F.       
Tokamak Theory (TOK), Max Planck Institute for Plasma Physics, Max Planck Society;

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Citation

Illerhaus, J., Treutterer, W., Sieglin, B., Bock, A., Fischer, R., Kudlacek, O., et al. (2024). Deep Learning-Based Real Time Electron Density Profile Reconstruction for Control at ASDEX Upgrade. Poster presented at 33rd Symposium on Fusion Technology (SOFT 2024), Dublin.


Cite as: https://hdl.handle.net/21.11116/0000-000F-F243-4
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