Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

Azarakhsh Jalalvand, Alan A. Kaptanoglu, Alvin V. Garcia, Andrew O. Nelson, Joseph Abbate, Max E. Austin, Geert Verdoolaege, Steven L. Brunton, William W. Heidbrink, Egemen Kolemen

Research output: Contribution to journalArticlepeer-review


Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfvén eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. Despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy.

Original languageEnglish (US)
Article number026007
JournalNuclear Fusion
Issue number2
StatePublished - Jan 2022


  • Alfvén eigenmodes
  • DIII-D
  • electron cyclotron emission
  • plasma control
  • reservoir computing networks

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Condensed Matter Physics


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