2022 Review of Data-Driven Plasma Science

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

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final Section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary Section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required.

Original languageEnglish (US)
Pages (from-to)1750-1838
Number of pages89
JournalIEEE Transactions on Plasma Science
Volume51
Issue number7
DOIs
StatePublished - Jul 1 2023

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Condensed Matter Physics

Keywords

  • Artificial intelligence
  • data-driven plasma science
  • machine learning
  • nuclear fusion
  • plasma control
  • plasma diagnostics
  • plasma processing
  • plasma simulation

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