Data-driven Science and Machine Learning Methods in Laser-Plasma Physics

• Main Authors: Döpp, Andreas, Eberle, Christoph, Howard, Sunny, Irshad, Faran, Lin, Jinpu, Streeter, Matthew
• Format: Journal Article
• Language: English
• Published: 30-11-2022
• Subjects: Computer Science - Learning; Physics - Accelerator Physics; Physics - Optics; Physics - Plasma Physics
• DOI: 10.48550/arxiv.2212.00026

High Power Laser Science and Engineering (2023) Laser-plasma physics has developed rapidly over the past few decades as
high-power lasers have become both increasingly powerful and more widely
available. Early experimental and numerical research in this field was
restricted to single-shot experiments with limited parameter exploration.
However, recent technological improvements make it possible to gather an
increasing amount of data, both in 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
effectively deal with situations in which still only sparse amounts of data are
available. This paper aims to present an overview of relevant machine learning
methods with focus on applicability to laser-plasma physics, including its
important sub-fields of laser-plasma acceleration and inertial confinement
fusion.