Machine learning characterisation of Alfvénic and sub-Alfvénic chirping and correlation with fast ion loss at NSTX

Woods, BJQ orcid.org/0000-0003-2628-3873, Duarte, VN, Fredrickson, ED et al. (3 more authors) (2020) Machine learning characterisation of Alfvénic and sub-Alfvénic chirping and correlation with fast ion loss at NSTX. IEEE Transactions on Plasma Science, 48 (1). pp. 71-81. ISSN 0093-3813

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Keywords: Machine learning (ML), plasma physics, tokamak physics.
Dates:
  • Published: January 2020
  • Accepted: 12 December 2019
  • Published (online): 22 January 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Maths and Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 28 Jan 2020 13:27
Last Modified: 21 Feb 2020 10:52
Status: Published
Publisher: IEEE
Identification Number: https://doi.org/10.1109/TPS.2019.2960206

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