Tokamak edge localized mode onset prediction with deep neural network and pedestal turbulence

Semin Joung, David R. Smith, G. McKee, Z. Yan, K. Gill, J. Zimmerman, B. Geiger, R. Coffee, F. H. O’Shea, A. Jalalvand, Egemen Kolemen

Research output: Contribution to journalArticlepeer-review


A neural network, BES-ELMnet, predicting a quasi-periodic disruptive eruption of the plasma energy and particles known as edge localized mode (ELM) onset is developed with observed pedestal turbulence from the beam emission spectroscopy system in DIII-D. BES-ELMnet has convolutional and fully-connected layers, taking two-dimensional plasma fluctuations with a temporal window of size 128 µs and generating a scalar output which can be interpreted as a probability of the upcoming ELM onset. As approximately labeled inter-ELM broadband ( 15 kHz ⩽ f ⩽ 150 kHz ) fluctuations are given to the network, BES-ELMnet learns by itself ELM-related precursors arising before the onsets through supervised learning scheme. BES-ELMnet achieves the gradually increasing ELM onset probabilities between two consecutive ELMs during the inter-ELM phases and can forecast the first ELM onsets which occur after the high confinement mode transition. We further investigate the network generality in terms of the selected frequency band to ensure the use of BES-ELMnet for various operation regimes without changing the trained architecture. Therefore, our novel prediction method will enhance a proactive high confinement mode control of fusion-grade plasmas.

Original languageEnglish (US)
Article number066038
JournalNuclear Fusion
Issue number6
StatePublished - Jun 2024

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Condensed Matter Physics


  • beam emission spectroscopy
  • deep neural network
  • DIII-D
  • edge localized mode
  • ELM onset prediction
  • turbulence


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