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  Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks

Schmidt, B. S., Salewski, M., Reman, B., Dendy, R. O., Moseev, D., Ochoukov, R., et al. (2021). Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks. Journal of Applied Physics, 130: 053528. doi:10.1063/5.0041456.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0008-FC60-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0009-936C-8
Genre: Journal Article

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schmidt_determining.pdf (Supplementary material), 4MB
 
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https://doi.org/10.1063/5.0041456 (Publisher version)
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https://orbit.dtu.dk/files/246610013/Schmidt2021rsi_postprint.pdf (Any fulltext)
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 Creators:
Schmidt, B. S.1, Author
Salewski, M.1, Author
Reman, B.1, Author
Dendy, R. O.1, Author
Moseev, D.2, 3, Author           
Ochoukov, R.2, Author           
Fasoli, A.1, Author
Baquero-Ruiz, M.1, Author
Järleblad, H.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Tokamak Scenario Development (E1), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856321              
3Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society, ou_2040305              

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Language(s): eng - English
 Dates: 2021
 Publication Status: Issued
 Pages: 5 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0041456
 Degree: -

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

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Title: Journal of Applied Physics
  Abbreviation : J. Appl. Phys.
Source Genre: Journal
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Publ. Info: New York, NY : AIP Publishing
Pages: - Volume / Issue: 130 Sequence Number: 053528 Start / End Page: - Identifier: ISSN: 0021-8979
CoNE: https://pure.mpg.de/cone/journals/resource/991042723401880