首页|Study Data from Chinese Academy of Sciences Update Knowledge of Machine Learning (An Experimental Application of Machine Learning Algorithms To Optimize the Fel Lasing Via Beam Trajectory Tuning At Dalian Coherent Light Source)
Study Data from Chinese Academy of Sciences Update Knowledge of Machine Learning (An Experimental Application of Machine Learning Algorithms To Optimize the Fel Lasing Via Beam Trajectory Tuning At Dalian Coherent Light Source)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Dalian, People's Repub lic of China, by NewsRx correspondents, research stated, "The lasing optimizatio n of Free -Electron Laser (FEL) facilities is a time-consuming and challenging t ask. Instead of operating manually by experienced operators, implementation of m achine learning algorithms offers a rapid and adaptable approach for FEL lasing optimization." Funders for this research include National Key R & D Program of Ch ina, National Natural Science Foundation of China (NSFC), Scientific Instrument Developing Project of Chinese Academy of Science, DICP, China Postdoctoral Scien ce Foundation, Specific Research Assistant Funding Program from Chinese Academy of Sciences, Pre-study Project of Dalian Advanced Light Source from city of Dali an. Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "Recently, such an experiment has been conducted at the vacuum ultravi olet FEL facility - Dalian Coherent Light Source (DCLS). Four algorithms, namely the standard and the neural network -based genetic algorithms, the deep determi nistic policy gradient and the soft actor critic reinforcement learning algorith ms, have been employed to enhance the FEL intensity by optimizing the electron b eam trajectory. These algorithms have shown notable efficacy in enhancing the FE L lasing, especially the reinforcement learning ones which achieved convergence within only approximately 400 iterations."
DalianPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningReinforcement Le arningChinese Academy of Sciences
NETL
NSTL
