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Physics > Plasma Physics

arXiv:2404.12416 (physics)
[Submitted on 18 Apr 2024]

Title:Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks

Authors:Ian Char, Youngseog Chung, Joseph Abbate, Egemen Kolemen, Jeff Schneider
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Abstract:Although tokamaks are one of the most promising devices for realizing nuclear fusion as an energy source, there are still key obstacles when it comes to understanding the dynamics of the plasma and controlling it. As such, it is crucial that high quality models are developed to assist in overcoming these obstacles. In this work, we take an entirely data driven approach to learn such a model. In particular, we use historical data from the DIII-D tokamak to train a deep recurrent network that is able to predict the full time evolution of plasma discharges (or "shots"). Following this, we investigate how different training and inference procedures affect the quality and calibration of the shot predictions.
Subjects: Plasma Physics (physics.plasm-ph); Machine Learning (cs.LG)
Cite as: arXiv:2404.12416 [physics.plasm-ph]
  (or arXiv:2404.12416v1 [physics.plasm-ph] for this version)
  https://doi.org/10.48550/arXiv.2404.12416

Submission history

From: Ian Char [view email]
[v1] Thu, 18 Apr 2024 00:05:57 UTC (2,310 KB)
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