2022 Review of Data-Driven Plasma Science

Anirudh, Rushil; Shalloo, Rob J.; Kirchen, Manuel; Budé, Rick H. S.; Lapenta, Giovanni; Mesbah, Ali; Yokoyama, Tatsuya; Wang, Zhehui; Zhang, Xinhua; Churchill, R. M.; Thiagarajan, Jayaraman; Becker, Markus M.; Chang, C. S.; Kim, Gon-Ho; Kline, John L.; Bremer, Peer-Timo; Gaffney, Jim A.; Shinagawa, Jun; Wang, Haimin; Citrin, Jonathan; Scheinker, Alexander; Kawaguchi, Satoru; Archibald, Rick; Kustowski, Bogdan; Sammuli, Brian; Gekelman, Walter; Najm, Habib N.; Xu, Xueqiao; Gibbs, Tom; Schissel, David P.; Li, Wenting; Klasky, Scott; Michoski, Craig; Olofsson, K. Erik J.; van Dijk, Jan; Probst, Michael; Chen, Lei; Krushelnick, Karl; Ticoş, Catalin M.; Munson, Todd; Park, Seolhye; Gainaru, Ana; Mason, Nigel J.; Asif, M. Salman; Yamada, Hiroshi; Ma, Tammy; Humbird, Kelli; Tennyson, Jonathan; Benkadda, Sadruddin; Hamaguchi, Satoshi; Wende, Kristian; Ventzek, Peter; Wang, Jason T. L.; Trieschmann, Jan; Murakami, Izumi; Seong, Jaegu; Spears, Brian K.; Jalas, Sören; Sawlani, Kapil; Peterson, J. Luc; Van Essen, Brian; Hill, Christian; Pugmire, Dave

Preprint

PUBDB-2022-05297

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. ; Hill, C. ; Humbird, K. ; Jalas, S.DESY* ; Kawaguchi, S. ; Kim, G.-H. ; Kirchen, M.Extern*DESY* ; Klasky, S. ; Kline, J. L. ; Krushelnick, K. ; Kustowski, B. ; Lapenta, G. ; Li, W. ; Ma, T. ; 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. (Corresponding author) ; Wende, K.Extern* ; Xu, X. ; Yamada, H. ; Yokoyama, T. ; Zhang, X.

2022

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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.

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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