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Neural networks: from image recognition to tokamak plasma tomography
Published online by Cambridge University Press: 17 April 2019
Abstract
In this paper, the possibility of using neural networks for fast tomographic reconstructions of tokamak plasma soft X-ray (SXR) emissivity is investigated. Indeed, the radiative cooling of heavy impurities like tungsten could be detrimental for the plasma core performances of ITER, thus developing robust and fast SXR diagnostic tools is a crucial issue to monitor the impurities and to mitigate in real-time their central accumulation. As preliminary work, a database of emissivity phantoms with associated synthetic measurements is used to train the neural network to solve the inversion problem. The inversion method, training process, and first tomographic reconstructions are presented with the perspectives about our future work.
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- Copyright © Cambridge University Press 2019
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