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

TLDR

Laser‑plasma physics has rapidly advanced with powerful lasers enabling large‑scale data collection, prompting interest in applying advanced mathematical, statistical, and computer‑science techniques to analyze big data and sparse datasets. This paper aims to present an overview of machine‑learning methods applicable to laser‑plasma physics, laser‑plasma acceleration, and inertial confinement fusion. The authors review and discuss machine‑learning techniques that can be applied to these sub‑fields, highlighting their relevance and potential benefits.

Abstract

Abstract 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.