Researching | Data-driven science and machine learning methods in laser–plasma physics

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 |Show fewer author(s)

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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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 manufacturing

Instrumentation, Measurement and Metrology