Data-driven models in fusion exhaust: AI methods and perspectives
Journal article, 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

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 086046

Implementation 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

More information

Latest update

7/31/2024