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  Multi-Class and Cross-Tokamak Disruption Prediction using Shapelet-Based Neural Networks

Artigues, V. (2023). Multi-Class and Cross-Tokamak Disruption Prediction using Shapelet-Based Neural Networks (PhD Thesis, Technische Universität München, München, 2023).

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-DA6D-4 Version Permalink: https://hdl.handle.net/21.11116/0000-000E-5600-1
Genre: Thesis
Other : Multi-Klassen und Tokamak-Übergreifende Disruptionsvorhersage mittels Shapelet-Basierter Neuronaler Netze

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artigues_multi-class.pdf (Supplementary material), 17MB
 
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https://mediatum.ub.tum.de/?id=1688550 (Supplementary material)
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 Creators:
Artigues, V.1, Author                 
Affiliations:
1Tokamak Theory (TOK), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856309              

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Language(s): eng - English
 Dates: 2023
 Publication Status: Published online
 Pages: 155 p.
 Publishing info: München : Technische Universität München
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: IPP 2024-01
 Degree: PhD

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