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.

machine learning

modeling

AI methods

exhaust

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

Teknologian Tutkimuskeskus (VTT)

Helsingin Yliopisto

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

Technische Universiteit Eindhoven

Ecole Polytechnique Federale de Lausanne (EPFL)

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 086046

Implementation 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

Mer information

Senast uppdaterat

2024-07-31