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Article Dans Une Revue The European Physical Journal D : Atomic, molecular, optical and plasma physics Année : 2023

Application of machine learning to spectroscopic line emission by hydrogen isotopes in fusion devices for isotopic ratio determination and prediction

M. Koubiti
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Résumé

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 H-D and D-T fusion plasmas.
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hal-04529871 , version 1 (02-04-2024)

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M. Koubiti. Application of machine learning to spectroscopic line emission by hydrogen isotopes in fusion devices for isotopic ratio determination and prediction. The European Physical Journal D : Atomic, molecular, optical and plasma physics, 2023, 77 (7), pp.137. ⟨10.1140/epjd/s10053-023-00719-0⟩. ⟨hal-04529871⟩

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