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

作　　者：Andreas Döpp Christoph Eberle Sunny Howard Faran Irshad Jinpu Lin Matthew Streeter 

机构地区：[1]Ludwig-Maximilians-Universität München,Garching,Germany [2]Department of Physics,Clarendon Laboratory,University of Oxford,Oxford,UK [3]School for Mathematics and Physics,Queen’s University Belfast,Belfast,UK

出　　处：《High Power Laser Science and Engineering》2023年第5期10-50,共41页高功率激光科学与工程（英文版）

基　　金：The authors acknowledge the use of GPT-3[288](text-davinci-003)in the copy-editing process of this manuscript.

摘　　要：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.

关 键 词：deep learning laser-plasma interaction machine learning 

分 类 号：TN24[电子电信—物理电子学] O53[理学—等离子体物理] TP181[理学—物理]