Machine-learning guided optimization of laser pulses for direct-drive implosions  

作　　者：Fuyuan Wu Xiaohu Yang Yanyun Ma Qi Zhang Zhe Zhang Xiaohui Yuan Hao Liu Zhengdong Liu Jiayong Zhong Jian Zheng Yutong Li Jie Zhang 

机构地区：[1]Key Laboratory for Laser Plasmas(MOE)and School of Physics and Astronomy,Shanghai Jiao Tong Universiry,Shanghai 200240,China [2]Collaborative Innovation Center of IFSA,Shanghai Jiao Tong University,Shanghai 200240,China [3]Department of Physics,National Universiy of Defense Technology,Changsha 410073,China [4]Beiing National Laboratory for Condensed Matter Physics,Institute of Physics,Chinese Academy of Sciences,Bejing 100190,China [5]Department of Astronomy,Beijing Normal Universiny,Beijing 100875,China [6]Department of Plasma Physics and Fusion Engineering,University of Science and Technology of China,Hefei 230026,China

出　　处：《High Power Laser Science and Engineering》2022年第2期35-41,共7页高功率激光科学与工程（英文版）

基　　金：the Strategic Priority Research Program of Chinese Academy of Sciences(Nos.XDA25051200 and XDA25050200);Startup Fund for Young Faculty at SJTU(No.21X010500627)。

摘　　要：The optimization of laser pulse shapes is of great importance and a major challenge for laser direct-drive implosions.In this paper,we propose an efficient intelligent method to perform laser pulse optimization via hydrodynamic simulations guided by the genetic algorithm and random forest algorithm.Compared to manual optimizations,the machine-learning guided method is able to efficiently improve the areal density by a factor of 63%and reduce the in-fiight-aspect ratio by a factor of 30%at the same time.A relationship between the maximum areal density and ion temperature is also achieved by the analysis of the big simulation dataset.This design method has been successfully demonstrated by the2021 summer double-cone ignition experiments conducted at the SG-II upgrade laser facility and has great prospects for the design of other inertial fusion experiments.

关 键 词：double-cone ignition genetic algorithm pulse optimization random forest 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TL632.1[自动化与计算机技术—控制科学与工程]