Journal Article

PUBDB-2022-02530

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png 2022 Review of Data-Driven Plasma Science

Anirudh, R. ; Archibald, R. ; Asif, M. S. ; Becker, M. M. ; Benkadda, S. ; Bremer, P.-T. ; Budé, R. H. S. ; Chang, C. S. ; Chen, L. ; Churchill, R. M. ; Citrin, J. ; Gaffney, J. A. ; Gainaru, A. ; Gekelman, W. ; Gibbs, T. ; Hamaguchi, S. (Corresponding author) ; Hill, C. ; Humbird, K. ; Jalas, S.CFEL*Extern* ; Kawaguchi, S. ; Kim, G.-H. ; Kirchen, M.Extern*DESY* ; Klasky, S. ; Kline, J. L. ; Krushelnick, K. ; Kustowski, B. ; Lapenta, G. ; Li, W. ; Ma, T.Extern* ; Mason, N. J. ; Mesbah, A. ; Michoski, C. ; Munson, T. ; Murakami, I. ; Najm, H. N. ; Olofsson, K. E. J. ; Park, S. ; Peterson, J. L. ; Probst, M. ; Pugmire, D. ; Sammuli, B. ; Sawlani, K. ; Scheinker, A. ; Schissel, D. P. ; Shalloo, R. J.DESY* ; Shinagawa, J. ; Seong, J. ; Spears, B. K. ; Tennyson, J. ; Thiagarajan, J. ; Ticoş, C. M. ; Trieschmann, J. ; van Dijk, J. ; Van Essen, B. ; Ventzek, P. ; Wang, H. ; Wang, J. T. L. ; Wang, Z. ; Wende, K.Extern* ; Xu, X. ; Yamada, H. ; Yokoyama, T. ; Zhang, X.

2023
IEEE New York, NY

This record in other databases:        

Please use a persistent id in citations: doi:10.1109/TPS.2023.3268170  doi:10.3204/PUBDB-2022-02530

Report No.: LA-UR-22-24834; arXiv:2205.15832

Abstract: Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.

Keyword(s): Plasma Physics (physics.plasm-ph) ; FOS: Physical sciences

Note: 112 pages (including 700+ references), 44 figures, submitted to IEEE Transactions on Plasma Science as a part of the IEEE Golden Anniversary Special Issue

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Contributing Institute(s):

1. Plasma Accelerators (MPA)
2. Laser für Plasmabeschleunigung (MLS)

Research Program(s):

1. 621 - Accelerator Research and Development (POF4-621) (POF4-621)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2023

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Preprint Anirudh, R. ; Archibald, R. ; Asif, M. S. ; et al
2022 Review of Data-Driven Plasma Science
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