Data-driven models in fusion exhaust: AI methods and perspectives
Artikel i vetenskaplig tidskrift, 2024
A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved modeling allowing more flexible integration of physics models in the light of extrapolations towards future fusion devices. The project encompasses various research objectives: (a) developments of surrogate model predictors for power & particle exhaust in fusion power plants; (b) assessments of surrogate models for time-dependent phenomena in the plasma-edge; (c) feasibility studies of micro-macro model discovery for plasma-facing components surface morphology & durability; and (d) enhancements of pedestal models & databases through interpolators and generators exploiting uncertainty quantification. Presented results demonstrate useful applications for machine-learning and artificial intelligence in fusion exhaust modeling schemes, enabling an unprecedented combination of both fast and accurate simulation.
modeling
exhaust
machine learning
AI methods
Författare
S. Wiesen
Forschungszentrum Jülich
Dutch Institute for Fundamental Energy Research (DIFFER)
S. Dasbach
Forschungszentrum Jülich
Heinrich Heine Universität Düsseldorf
A. Kit
Helsingin Yliopisto
A. Järvinen
Helsingin Yliopisto
Teknologian Tutkimuskeskus (VTT)
Andreas Gillgren
Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik
A. Ho
Dutch Institute for Fundamental Energy Research (DIFFER)
Technische Universiteit Eindhoven
A. Panera
Dutch Institute for Fundamental Energy Research (DIFFER)
D. Reiser
Forschungszentrum Jülich
M. Brenzke
Forschungszentrum Jülich
Y. Poels
Ecole Polytechnique Federale de Lausanne (EPFL)
Technische Universiteit Eindhoven
E. Westerhof
Dutch Institute for Fundamental Energy Research (DIFFER)
V. Menkovski
Technische Universiteit Eindhoven
G. F. Derks
Dutch Institute for Fundamental Energy Research (DIFFER)
Pär Strand
Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik
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
Europeiska kommissionen (EU) (101052200), 2021-01-01 -- 2025-12-31.
Ämneskategorier
Subatomär fysik
Beräkningsmatematik
Datavetenskap (datalogi)
DOI
10.1088/1741-4326/ad5a1d