TY  - JOUR
AU  - Anirudh, Rushil
AU  - Archibald, Rick
AU  - Asif, M. Salman
AU  - Becker, Markus M.
AU  - Benkadda, Sadruddin
AU  - Bremer, Peer-Timo
AU  - Budé, Rick H. S.
AU  - Chang, C. S.
AU  - Chen, Lei
AU  - Churchill, R. M.
AU  - Citrin, Jonathan
AU  - Gaffney, Jim A.
AU  - Gainaru, Ana
AU  - Gekelman, Walter
AU  - Gibbs, Tom
AU  - Hamaguchi, Satoshi
AU  - Hill, Christian
AU  - Humbird, Kelli
AU  - Jalas, Sören
AU  - Kawaguchi, Satoru
AU  - Kim, Gon-Ho
AU  - Kirchen, Manuel
AU  - Klasky, Scott
AU  - Kline, John L.
AU  - Krushelnick, Karl
AU  - Kustowski, Bogdan
AU  - Lapenta, Giovanni
AU  - Li, Wenting
AU  - Ma, Tammy
AU  - Mason, Nigel J.
AU  - Mesbah, Ali
AU  - Michoski, Craig
AU  - Munson, Todd
AU  - Murakami, Izumi
AU  - Najm, Habib N.
AU  - Olofsson, K. Erik J.
AU  - Park, Seolhye
AU  - Peterson, J. Luc
AU  - Probst, Michael
AU  - Pugmire, Dave
AU  - Sammuli, Brian
AU  - Sawlani, Kapil
AU  - Scheinker, Alexander
AU  - Schissel, David P.
AU  - Shalloo, Rob J.
AU  - Shinagawa, Jun
AU  - Seong, Jaegu
AU  - Spears, Brian K.
AU  - Tennyson, Jonathan
AU  - Thiagarajan, Jayaraman
AU  - Ticoş, Catalin M.
AU  - Trieschmann, Jan
AU  - van Dijk, Jan
AU  - Van Essen, Brian
AU  - Ventzek, Peter
AU  - Wang, Haimin
AU  - Wang, Jason T. L.
AU  - Wang, Zhehui
AU  - Wende, Kristian
AU  - Xu, Xueqiao
AU  - Yamada, Hiroshi
AU  - Yokoyama, Tatsuya
AU  - Zhang, Xinhua
TI  - 2022 Review of Data-Driven Plasma Science
JO  - IEEE transactions on plasma science
VL  - 51
IS  - 7
SN  - 0093-3813
CY  - New York, NY
PB  - IEEE
M1  - PUBDB-2022-02530
M1  - arXiv:2205.15832
M1  - LA-UR-22-24834
SP  - 1750 - 1838
PY  - 2023
N1  - 112 pages (including 700+ references), 44 figures, submitted to IEEE Transactions on Plasma Science as a part of the IEEE Golden Anniversary Special Issue
AB  - 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.
KW  - Plasma Physics (physics.plasm-ph) (Other)
KW  - FOS: Physical sciences (Other)
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001048298900001
DO  - DOI:10.1109/TPS.2023.3268170
UR  - https://bib-pubdb1.desy.de/record/478314
ER  -