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- Rushil Anirudh
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Rick Archibald
- Oak Ridge National Laboratory, Department of Electrical and Computer Engineering, Oak Ridge, TN, USA
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- M. Salman Asif
- University of California at Riverside, Riverside, CA, USA
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- Markus M. Becker
- Leibniz Institute for Plasma Science and Technology (INP), Greifswald, Germany
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- Sadruddin Benkadda
- CNRS, PIIM UMR 7345, Aix-Marseille University, Marseille, France
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- Peer-Timo Bremer
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Rick H. S. Budé
- Department of Applied Physics, Eindhoven University of Technology, Eindhoven, The Netherlands
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- C. S. Chang
- Princeton Plasma Physics Laboratory, Princeton, NJ, USA
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- Lei Chen
- Institute of Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria
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- R. M. Churchill
- Princeton Plasma Physics Laboratory, Princeton, NJ, USA
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- Jonathan Citrin
- Dutch Institute for Fundamental Energy Research (DIFFER), Eindhoven, The Netherlands
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- Jim A. Gaffney
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Ana Gainaru
- Oak Ridge National Laboratory, Department of Electrical and Computer Engineering, Oak Ridge, TN, USA
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- Walter Gekelman
- Department of Physics and Astronomy, University of California at Los Angeles, Los Angeles, CA, USA
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- Tom Gibbs
- NVIDIA, Santa Clara, CA, USA
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- Satoshi Hamaguchi
- Center for Atomic and Molecular Technologies, Graduate School of Engineering, Osaka University, Osaka, Japan
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- Christian Hill
- Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
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- Kelli Humbird
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Sören Jalas
- Center for Free-Electron Laser Science and the Department of Physics, Universität Hamburg, Hamburg, Germany
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- Satoru Kawaguchi
- Division of Information and Electronic Engineering, Graduate School of Engineering, Muroran Institute of Technology, Muroran, Japan
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- Gon-Ho Kim
- Department of Nuclear Engineering, Seoul National University, Seoul, Republic of Korea
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- Manuel Kirchen
- Deutsches Elektronen-Synchrotron (DESY), Hamburg, Germany
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- Scott Klasky
- Oak Ridge National Laboratory, Department of Electrical and Computer Engineering, Oak Ridge, TN, USA
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- John L. Kline
- Los Alamos National Laboratory, Los Alamos, NM, USA
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- Karl Krushelnick
- Center for Ultrafast Optical Science, University of Michigan, Ann Arbor, MI, USA
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- Bogdan Kustowski
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Giovanni Lapenta
- Department of Mathematics, University of Leuven (KU Leuven), Leuven, Belgium
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- Wenting Li
- Los Alamos National Laboratory, Los Alamos, NM, USA
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- Tammy Ma
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Nigel J. Mason
- Department of Physical Sciences, University of Kent, Canterbury, U.K.
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- Ali Mesbah
- Department of Chemical and Biomolecular Engineering, University of California at Berkeley, Berkeley, CA, USA
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- Craig Michoski
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
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- Todd Munson
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
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- Izumi Murakami
- National Institute for Fusion Science, National Institutes of Natural Sciences, Gifu, Japan
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- Habib N. Najm
- Sandia National Laboratories, Livermore, CA, USA
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- K. Erik J. Olofsson
- General Atomics, San Diego, CA, USA
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- Seolhye Park
- Samsung Display Company Ltd., Asan-si, Republic of Korea
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- J. Luc Peterson
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Michael Probst
- Institute of Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria
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- David Pugmire
- Oak Ridge National Laboratory, Department of Electrical and Computer Engineering, Oak Ridge, TN, USA
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- Brian Sammuli
- General Atomics, San Diego, CA, USA
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- Kapil Sawlani
- Lam Research Corporation, Fremont, CA, USA
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- Alexander Scheinker
- Los Alamos National Laboratory, Los Alamos, NM, USA
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- David P. Schissel
- General Atomics, San Diego, CA, USA
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- Rob J. Shalloo
- Deutsches Elektronen-Synchrotron (DESY), Hamburg, Germany
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- Jun Shinagawa
- Tokyo Electron America, Inc., Austin, TX, USA
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- Jaegu Seong
- Samsung Display Company Ltd., Asan-si, Republic of Korea
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- Brian K. Spears
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Jonathan Tennyson
- Department of Physics and Astronomy, University College London, London, U.K.
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- Jayaraman Thiagarajan
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Catalin M. Ticoş
- National Institute for Laser, Plasma and Radiation Physics, Măgurele, Romania
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- Jan Trieschmann
- Theoretical Electrical Engineering, Faculty of Engineering, Kiel University, Kiel, Germany
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- Jan van Dijk
- Department of Applied Physics, Eindhoven University of Technology, Eindhoven, The Netherlands
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- Brian Van Essen
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Peter Ventzek
- Tokyo Electron America, Inc., Austin, TX, USA
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- Haimin Wang
- Institute for Space Weather Sciences, New Jersey Institute of Technology, Newark, NJ, USA
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- Jason T. L. Wang
- Institute for Space Weather Sciences, New Jersey Institute of Technology, Newark, NJ, USA
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- Zhehui Wang
- Los Alamos National Laboratory, Los Alamos, NM, USA
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- Kristian Wende
- Leibniz Institute for Plasma Science and Technology (INP), Greifswald, Germany
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- Xueqiao Xu
- Lawrence Livermore National Laboratory, Livermore, CA, USA
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- Hiroshi Yamada
- Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
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- Tatsuya Yokoyama
- Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
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- Xinhua Zhang
- Los Alamos National Laboratory, Los Alamos, NM, USA
書誌事項
- 公開日
- 2023-07
- 資源種別
- journal article
- 権利情報
-
- https://creativecommons.org/licenses/by/4.0/legalcode
- https://creativecommons.org/licenses/by/4.0/legalcode
- DOI
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- 10.1109/tps.2023.3268170
- 10.48550/arxiv.2205.15832
- 10.3204/pubdb-2022-02530
- 公開者
- Institute of Electrical and Electronics Engineers (IEEE)
この論文をさがす
説明
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.
112 pages (including 700+ references), 44 figures, submitted to IEEE Transactions on Plasma Science as a part of the IEEE Golden Anniversary Special Issue
収録刊行物
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- IEEE Transactions on Plasma Science
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IEEE Transactions on Plasma Science 51 (7), 1750-1838, 2023-07
Institute of Electrical and Electronics Engineers (IEEE)
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キーワード
- Artificial intelligence
- VIRTUAL METROLOGY
- PROFILE SIMULATION
- plasma simulation
- Plasma processing
- FOS: Physical sciences
- Contracts
- 530
- Data science
- Plasma diagnostics
- Research and development
- Physics, Fluids & Plasmas
- Machine learning
- [INFO]Computer Science [cs]
- POLYNOMIAL CHAOS
- UNCERTAINTY QUANTIFICATION
- info:eu-repo/classification/ddc/530
- Scientific computing
- ELECTRON-ENERGY DISTRIBUTION
- nuclear fusion
- OF-THE-ART
- plasma processing
- [PHYS]Physics [physics]
- Science & Technology
- plasma diagnostics
- Physics
- data-driven plasma science
- INERTIAL CONFINEMENT FUSION
- Technological innovation
- 001
- DIFFERENTIAL-EQUATIONS
- Data-driven plasma science
- Plasma control
- Physics - Plasma Physics
- Plasma simulation.
- Plasma Physics (physics.plasm-ph)
- STOCHASTIC PROJECTION METHOD
- machine learning
- Plasmas
- plasma control
- Physical Sciences
- Nuclear fusion
- PARAMETER-ESTIMATION
詳細情報 詳細情報について
-
- CRID
- 1360302864782500864
-
- ISSN
- 19399375
- 00933813
-
- 資料種別
- journal article
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- データソース種別
-
- Crossref
- KAKEN
- OpenAIRE