High Power Laser Science and Engineering, Volume. , Issue , ()

Data-driven Science and Machine Learning Methods in Laser-Plasma Physics [Early Posting]

Döpp Andreas, Eberle Christoph, Howard Sunny, Irshad Faran, Lin Jinpu, Streeter Matthew
Author Affiliations
  • LMU
  • University of Oxford
  • Ludwig Maximilians University Munich
  • Queen''''s University Belfast
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    Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available. Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations. This has sparked interest in using advanced techniques from mathematics, statistics and computer science to deal with, and benefit from, big data. At the same time, sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion.

    Paper Information

    Manuscript Accepted: Jan. 29, 2023

    Posted: May. 26, 2023

    DOI: HPL-0149