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  Deep learning for Gaussian process soft x-ray tomography model selection in the ASDEX Upgrade tokamak

Matos, F., Svensson, J., Pavone, A., Odstrcil, T., & Jenko, F. (2020). Deep learning for Gaussian process soft x-ray tomography model selection in the ASDEX Upgrade tokamak. Review of Scientific Instruments, 91: 103501. doi:10.1063/5.0020680.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0007-43D3-F Version Permalink: http://hdl.handle.net/21.11116/0000-0007-481C-A
Genre: Journal Article

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https://doi.org/10.1063/5.0020680 (Publisher version)
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 Creators:
Matos, F.1, Author              
Svensson, J.2, Author              
Pavone, A.3, Author              
Odstrcil, T.4, Author
Jenko, F.1, Author              
Affiliations:
1Tokamak Theory (TOK), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856309              
2Stellarator Dynamics and Transport (E5), Max Planck Institute for Plasma Physics, Max Planck Society, ou_2040306              
3Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society, ou_2040305              
4External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 20202020
 Publication Status: Published in print
 Pages: 13 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0020680
 Degree: -

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Project name : Euratom Research and Training Programme 2014-2018 and 2019-2020 – EUROfusion
Grant ID : 633053
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Review of Scientific Instruments
  Abbreviation : Rev. Sci. Instrum.
Source Genre: Journal
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Publ. Info: Melville, NY : AIP Publishing
Pages: - Volume / Issue: 91 Sequence Number: 103501 Start / End Page: - Identifier: ISSN: 0034-6748
CoNE: https://pure.mpg.de/cone/journals/resource/991042742033452