典型文献
Machine learning-based direct solver for one-to-many problems on temporal shaping of relativistic electron beams
文献摘要：
To control the temporal profile of a relativistic electron beam to meet requirements of various advanced scientific applications like free-electron-laser and plasma wakefield acceleration,a widely-used technique is to manipulate the dispersion terms which turns out to be one-to-many prob-lems.Due to their intrinsic one-to-many property,current popular stochastic optimization approaches on temporal shaping may face the problems of long computing time or sometimes suggesting only one solu-tion.Here we propose a real-time solver for one-to-many problems of temporal shaping,with the aid of a semi-supervised machine learning method,the conditional generative adversarial network(CGAN).We demonstrate that the CGAN solver can learn the one-to-many dynamics and is able to accurately and quickly predict the required dispersion terms for different custom temporal profiles.This machine learning-based solver is expected to have the potential for wide applications to one-to-many problems in other scientific fields.
文献关键词：
中图分类号：
作者姓名：
Jinyu Wan;Yi Jiao
作者机构：
Key Laboratory of Particle Acceleration Physics and Technology,Institute of High Energy Physics,Chinese Academy of Sciences,Beijing 100049,China;University of Chinese Academy of Sciences,Beijing 100049,China;China Spallation Neutron Source,Dongguan 523803,China
文献出处：
引用格式：
[1]Jinyu Wan;Yi Jiao-.Machine learning-based direct solver for one-to-many problems on temporal shaping of relativistic electron beams)[J].物理学前沿,2022(06):15-24
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B类：
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AB值：
0.561207
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