Fast modeling of turbulent transport in fusion plasmas using neural networks-Reference-Cited by-同舟云学术

Fast modeling of turbulent transport in fusion plasmas using neural networks

Published:2020-02 Issue:2 Volume:27 Page:022310
ISSN:1070-664X
Container-title:Physics of Plasmas
language:en
Short-container-title:Physics of Plasmas

Author:

van de Plassche K. L.¹^ORCID,Citrin J.¹^ORCID,Bourdelle C.²,Camenen Y.³^ORCID,Casson F. J.⁴^ORCID,Dagnelie V. I.¹⁵,Felici F.⁶^ORCID,Ho A.¹^ORCID,Van Mulders S.⁶^ORCID,

Affiliation:

1. DIFFER, PO Box 6336, 5600 HH Eindhoven, The Netherlands

2. CEA, IRFM, F-13108 Saint-Paul-lez-Durance, France

3. CNRS, Aix-Marseille Univ., PIIM UMR7345, Marseille, France

4. CCFE, Culham Science Centre, OX14 3DB Abingdon, United Kingdom

5. ITP, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands

6. EPFL-SPC, CH-1015 Lausanne, Switzerland

Funder

H2020 Euratom

Publisher

AIP Publishing

Subject

Condensed Matter Physics

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5134126

Reference32 articles.

1. Integrated Tokamak modeling: When physics informs engineering and research planning

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4. Tractable flux-driven temperature, density, and rotation profile evolution with the quasilinear gyrokinetic transport model QuaLiKiz

5. Core turbulent transport in tokamak plasmas: bridging theory and experiment with QuaLiKiz

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