Data-driven profile prediction for DIII-D

J. Abbate, R. Conlin, E. Kolemen

Research output: Contribution to journalArticlepeer-review

Abstract

A new, fully data-driven algorithm has been developed that uses a neural network to predict plasma profiles on a scale of τ E into the future given an actuator trajectory and the plasma state history. The model was trained and tested on DIII-D data from the 2013-2018 experimental campaigns. The model runs in tens of milliseconds and is very simple to use. This makes it a potentially useful tool for operators and physicists when planning plasma scenarios. It is also fast enough to be used for real-time model-predictive control.

Original languageEnglish (US)
Article number046027
JournalNuclear Fusion
Volume61
Issue number4
DOIs
StatePublished - Apr 2021

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Condensed Matter Physics

Keywords

  • control
  • DIII-D
  • neural networks
  • transport

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