Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)

Nuclear Fushion

article

S.M. Gonzalez de Vicente¹  D. Mazon²  M. Xu³  S. Pinches⁴  M. Churchill⁵  A. Dinklage⁶  R. Fischer⁷  A. Murari⁸  P. Rodriguez-Fernandez⁹  J. Stillerman⁹  J. Vega^1,10  G. Verdoolaege^1,11 

[1] Department of Nuclear Sciences and Applications Vienna, International Atomic Energy Agency;CEA;Southwestern Institute of Physics Chengdu;ITER Organization;Princeton Plasma Physics Laboratory Princeton, Princeton;Max Planck Institut fuer Plasmaphysik Greifswald;Max Planck Institut fuer Plasmaphysik Garching;Consorzio RFX, CNR, ENEA, INFN, Università di Padova;MIT Plasma Science and Fusion Center;CIEMAT;Department of Applied Physics Gent, Ghent University

关键词: integrated data analysis;    data validation;    Bayesian techniques;    neural networks;    machine learning;    disruption predictors;    image processing;   

DOI  :  10.1088/1741-4326/acbfce

来源: Institute of Physics Publishing Ltd.

【 摘 要 】

The objective of the Fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to artificial intelligence for automatization and improving reliability of control processes.

【 授权许可】

Unknown