Application of machine learning to spectroscopic line emission by hydrogen isotopes in fusion devices for isotopic ratio determination and prediction
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.
Mots clés
Machine Learning, Plasma Spectroscopy, Hydrogen isotopic ratio, magnetic fusion, deep-learning, Artificial Intelligence
Machine Learning Plasma Spectroscopy Hydrogen isotopic ratio magnetic fusion deep-learning Artificial Intelligence
Machine Learning
Plasma Spectroscopy
Hydrogen isotopic ratio
magnetic fusion
deep-learning
Artificial Intelligence
Domaines
Physique [physics]
Origine : Fichiers produits par l'(les) auteur(s)