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  Neural network surrogates of Bayesian diagnostic models for fast inference of plasma parameters

Pavone, A., Svensson, J., Krychowiak, M., Hergenhahn, U., Winters, V., Kornejew, P., et al. (2021). Neural network surrogates of Bayesian diagnostic models for fast inference of plasma parameters. Review of Scientific Instruments, 92: 033531. doi:10.1063/5.0043772.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-F0F2-8 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-E925-4
Genre: Journal Article

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pavone_neural.pdf (Supplementary material), 4MB
 
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https://doi.org/10.1063/5.0043772 (Publisher version)
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Open Access
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 Creators:
Pavone, A.1, Author           
Svensson, J.2, Author           
Krychowiak, M.3, Author           
Hergenhahn, U.3, 4, Author           
Winters, V.2, Author           
Kornejew, P.3, Author           
Kwak, S.2, Author           
Hoefel, U.1, Author           
Koenig, R.3, Author           
Wolf, R. C.1, Author           
W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society, Author              
Affiliations:
1Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society, ou_2040305              
2Stellarator Dynamics and Transport (E5), Max Planck Institute for Plasma Physics, Max Planck Society, ou_2040306              
3Stellarator Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856286              
4External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 20202021
 Publication Status: Published online
 Pages: 5 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0043772
 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: 92 Sequence Number: 033531 Start / End Page: - Identifier: ISSN: 0034-6748
CoNE: https://pure.mpg.de/cone/journals/resource/991042742033452