Coincidence anomaly detection for unsupervised locating of edge localized modes in the DIII-D tokamak dataset

Using supervised learning to train a machine learning model to predict an on-coming edge localized mode (ELM) requires a large number of labeled samples. Creating an appropriate data set from the very large database of discharges at a long-running tokamak, such as DIII-D, would be a very time-consum...

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Autors principals:	Finn H O’Shea, Semin Joung, David R Smith, Daniel Ratner, Ryan Coffee
Format:	Artigo
Idioma:	Inglês
Publicat:	IOP Publishing 2024-01-01
Col·lecció:	Machine Learning: Science and Technology
Matèries:	machine learning unsupervised learning tokamak plasma fusion
Accés en línia:	https://doi.org/10.1088/2632-2153/ad6be7
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