https://doi.org/10.1140/epjd/s10053-023-00719-0
Regular Article – Plasma Physics
Application of machine learning to spectroscopic line emission by hydrogen isotopes in fusion devices for isotopic ratio determination and prediction
Aix-Marseille Université-CNRS, PIIM Lab UMR 7345, Avenue Escadrille Normandie-Niemen, 13397, Marseille Cedex 20, France
a
mohammed.koubiti@univ-amu.fr
Received:
7
March
2023
Accepted:
26
June
2023
Published online:
14
July
2023
Machine learning, a subfield of artificial intelligence, is being increasingly used in physics and other scientific domains for data analysis and predictions. This trend to use machine learning concerns now several plasma physics topics like those related to magnetic fusion. With the ongoing or planned buildings of larger tokamaks like ITER, magnetic fusion is a research field where artificial intelligence techniques can be of a great help. In this short communication, I will discuss in particular the use of machine learning in connection with plasma spectroscopy for the hydrogen isotopic ratio determination. In addition to some preliminary results, I will discuss some ideas and open questions related to predictions of isotopic ratio determination for HD and DT fusion plasmas.
T.I. : Physics of Ionized Gases and Spectroscopy of Isolated Complex Systems: Fundamentals and Applications. Guest editors: BratislavObradović, Jovan Cvetić, Dragana Ilić, Vladimir Srećković and Sylwia Ptasinska.
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© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
