Deep Learning for Plasma Tomography in Nuclear FusionDownload PDF

23 Oct 2020 (modified: 03 Dec 2020)NeurIPS 2020 Workshop Deep Inverse Blind SubmissionReaders: Everyone
  • Keywords: Deep Learning, Computed Tomography, Plasma Physics, Nuclear Fusion
  • TL;DR: Deep learning is used to perform the tomographic reconstruction of the plasma radiation profile inside a nuclear fusion device.
  • Abstract: Tomography is arguably one of the most representative examples of an inverse problem, where the shape of an object must be reconstructed from its projections over a limited number of lines of sight. The regularization that must be imposed to solve such an ill-posed problem often results in iterative algorithms that are computationally expensive and do not meet the requirements of real-time applications. Deep learning offers a promising approach to perform such reconstruction with sufficient accuracy, while being several orders of magnitude faster, to the point that it becomes possible to use tomography in real-time. In this paper, we give an example of how real-time tomography based on deep learning is being used to reconstruct the plasma radiation profile in a nuclear fusion device. The availability of such profile in real-time allows setting up new alarms in the real-time control system, with a view towards anticipating plasma disruptions.
  • Conference Poster: pdf
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