Data-driven Science and Machine Learning Methods in Laser-Plasma Physics
It offers a domain-specific review for researchers in laser-plasma physics, focusing on sub-fields like laser-plasma acceleration and inertial confinement fusion, but is incremental as it presents an overview rather than new findings.
This paper provides an overview of machine learning methods applicable to laser-plasma physics, addressing the challenge of handling increasing data from experiments and simulations while also managing sparse data scenarios.
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.