首页|Recent Findings from Shenyang Normal University Provides New Insights into Machi ne Learning (Importance of Physical Information On the Prediction of Heavy-ion F usion Cross Sections With Machine Learning)
Recent Findings from Shenyang Normal University Provides New Insights into Machi ne Learning (Importance of Physical Information On the Prediction of Heavy-ion F usion Cross Sections With Machine Learning)
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 Daily News - Research findings on Machine Learning are discussed in a new report. According to news reporting from Liaoning, People ’s Republic of China, by NewsRx journalists, research stated, “In this work, the Light Gradient Boosting Machine (LightGBM), which is a modern decision tree bas ed machine-learning algorithm, is used to study the fusion cross section (CS) of heavy -ion reaction. Several basic quantities (e.g., mass number and proton num ber of projectile and target) and the CS obtained from phenomenological formula are fed into the LightGBM algorithm to predict the CS.”
LiaoningPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningShenyang Normal University
NETL
NSTL
