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High Power Laser Science and Engineering, Volume. 11, Issue 5, 05000e55(2023)

Data-driven science and machine learning methods in laser–plasma physics Editors' Pick

Andreas Döpp1,2、*, Christoph Eberle1, Sunny Howard1,2, Faran Irshad1, Jinpu Lin1, and Matthew Streeter3
Author Affiliations
  • 1Ludwig-Maximilians-Universität München, Garching, Germany
  • 2Department of Physics, Clarendon Laboratory, University of Oxford, Oxford, UK
  • 3School for Mathematics and Physics, Queen’s University Belfast, Belfast, UK
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    Article index updated:Apr. 27, 2024

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    Andreas Döpp, Christoph Eberle, Sunny Howard, Faran Irshad, Jinpu Lin, Matthew Streeter. Data-driven science and machine learning methods in laser–plasma physics[J]. High Power Laser Science and Engineering, 2023, 11(5): 05000e55

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    Paper Information

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    Received: Nov. 30, 2022

    Accepted: May. 24, 2023

    Published Online: Aug. 18, 2023

    The Author Email: Andreas Döpp (a.doepp@lmu.de)

    DOI:10.1017/hpl.2023.47

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology