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

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Title: Data-driven models in fusion exhaust: AI methods and perspectives
Authors: S. Wiesen, S. Dasbach, A. Kit, A.E. Jaervinen, A. Gillgren, A. Ho, A. Panera, D. Reiser, M. Brenzke, Y. Poels, E. Westerhof, V. Menkovski, G.F. Derks, P. Strand
Source: Nuclear Fusion, Vol 64, Iss 8, p 086046 (2024)
Publisher Information: IOP Publishing, 2024.
Publication Year: 2024
Collection: LCC:Nuclear and particle physics. Atomic energy. Radioactivity
Subject Terms: exhaust, modeling, machine learning, AI methods, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
More Details: 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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1741-4326
0029-5515
Relation: https://doaj.org/toc/0029-5515
DOI: 10.1088/1741-4326/ad5a1d
Access URL: https://doaj.org/article/1812415a81664416b16d9d0446019781
Accession Number: edsdoj.1812415a81664416b16d9d0446019781
Database: Directory of Open Access Journals
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  Data: 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.
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