典型文献
Application of kernel ridge regression in predicting neutron-capture reaction cross-sections
文献摘要：
This article provides the first application of the machine-learning approach in the study of the cross-sections for neutron-capture reactions with the kernel ridge regression(KRR)approach.It is found that the KRR approach can reduce the root-mean-square(rms)deviation of the relative errors between the experimental data of the Maxwellian-averaged(n,y)cross-sections and the corresponding theoretical predictions from 69.8％to 35.4％.By including the data with different temperatures in the training set,the rms deviation can be further significantly reduced to 2.0％.Moreover,the extrapolation performance of the KRR approach along different temperatures is found to be effective and reliable.
文献关键词：
中图分类号：
作者姓名：
T X Huang;X H Wu;P W Zhao
作者机构：
State Key Laboratory of Nuclear Physics and Technology,School of Physics,Peking University,Beijing 100871,China
文献出处：
引用格式：
[1]T X Huang;X H Wu;P W Zhao-.Application of kernel ridge regression in predicting neutron-capture reaction cross-sections)[J].理论物理,2022(09):94-100
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AB值：
0.547386
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