A dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network

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Veröffentlicht in:Nuclear fusion Jg. 61; H. 12
Hauptverfasser: Wei, Y., Levesque, J.P., Hansen, C.J., Mauel, M.E., Navratil, G.A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IOP Publishing 01.12.2021
Schlagworte:
disruption avoidance
feedback control
HBT-EP
machine learning
neural network
variational autoencoder
ISSN:0029-5515, 1741-4326
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Author Mauel, M.E.
Navratil, G.A.
Hansen, C.J.
Levesque, J.P.
Wei, Y.
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  surname: Levesque
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  surname: Hansen
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  surname: Navratil
  fullname: Navratil, G.A.
  organization: Columbia University, United States of America Department of Applied Physics and Applied Mathematics
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Copyright 2021 The Author(s). Published on behalf of IAEA by IOP Publishing Ltd
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SubjectTerms disruption avoidance
feedback control
HBT-EP
machine learning
neural network
variational autoencoder
Title A dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network
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