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  Neural network regression approaches to reconstruct properties of magnetic configuration from Wendelstein 7-X modeled heat load patterns

Blatzheim, M., Böckenhoff, D., & W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society (2019). Neural network regression approaches to reconstruct properties of magnetic configuration from Wendelstein 7-X modeled heat load patterns. Nuclear Fusion, 59: 126029. doi:10.1088/1741-4326/ab4123.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0004-DCC8-3 Version Permalink: https://hdl.handle.net/21.11116/0000-0007-48C2-D
Genre: Journal Article

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blatzheim_neural network regression.pdf (Supplementary material), 3MB
 
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 Creators:
Blatzheim, M.1, 2, Author           
Böckenhoff, D.1, Author           
W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society, Author              
Affiliations:
1Stellarator Edge and Divertor Physics (E4), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856286              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 20192019
 Publication Status: Issued
 Pages: 13 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1088/1741-4326/ab4123
 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: Nuclear Fusion
Source Genre: Journal
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Publ. Info: Bristol; Vienna : IOP Publishing; IAEA
Pages: - Volume / Issue: 59 Sequence Number: 126029 Start / End Page: - Identifier: ISSN: 0029-5515
CoNE: https://pure.mpg.de/cone/journals/resource/991042749627140