Data-driven models in fusion exhaust: AI methods and perspectives
Journal article, 2024
machine learning
modeling
AI methods
exhaust
Author
S. Wiesen
Forschungszentrum Jülich
Dutch Institute for Fundamental Energy Research (DIFFER)
S. Dasbach
Forschungszentrum Jülich
Heinrich Heine University Düsseldorf
A. Kit
University of Helsinki
A. Järvinen
Technical Research Centre of Finland (VTT)
University of Helsinki
Andreas Gillgren
Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics
A. Ho
Dutch Institute for Fundamental Energy Research (DIFFER)
Eindhoven University of Technology
A. Panera
Dutch Institute for Fundamental Energy Research (DIFFER)
D. Reiser
Forschungszentrum Jülich
M. Brenzke
Forschungszentrum Jülich
Y. Poels
Eindhoven University of Technology
Swiss Federal Institute of Technology in Lausanne (EPFL)
E. Westerhof
Dutch Institute for Fundamental Energy Research (DIFFER)
V. Menkovski
Eindhoven University of Technology
G. F. Derks
Dutch Institute for Fundamental Energy Research (DIFFER)
Pär Strand
Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics
Nuclear Fusion
00295515 (ISSN) 17414326 (eISSN)
Vol. 64 8 086046Implementation of activities described in the Roadmap to Fusion during Horizon Europe through a joint programme of the members of the EUROfusion consortium
European Commission (EC) (101052200), 2021-01-01 -- 2025-12-31.
Subject Categories
Subatomic Physics
Computational Mathematics
Computer Science
DOI
10.1088/1741-4326/ad5a1d