109 citations · 327 across the 8 of their papers we have counts for
7 papers
Machine learning in nuclear physics at low and intermediate energies
Wanbing He, Qingfeng Li, Yugang Ma +3
Machine learning is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data proce…
Nuclear mass predictions with machine learning reaching the accuracy required by -process studies
Z. M. Niu, H. Z. Liang
Nuclear masses are predicted with the Bayesian neural networks by learning the mass surface of even-even nuclei and the correlation energies to their neighbouring nuclei. By keepin…
Probing the resonance in the Dirac equation with quadruple-deformed potentials by complex momentum representation method
Zhi Fang, Min Shi, Jian-You Guo +3
Resonance plays critical roles in the formation of many physical phenomena, and many techniques have been developed for the exploration of resonance. In a recent letter [Phys. Rev.…
Probing the resonance of Dirac particle by the application of complex momentum representation
Niu Li, Min Shi, Jian-You Guo +2
Resonance plays critical roles in the formation of many physical phenomena, and several methods have been developed for the exploration of resonance. In this work, we propose a new…
Improved radial basis function approach with the odd-even corrections
Z. M. Niu, B. H. Sun, H. Z. Liang +2
The radial basis function (RBF) approach has been used to improve the mass predictions of nuclear models. However, systematic deviations exist between the improved masses and the e…
Systematic calculations of -decay half-lives with an improved empirical formula
Z. Y. Wang, Z. M. Niu, Q. Liu +1
Based on the recent data in NUBASE2012, an improved empirical formula for evaluating the -decay half-lives is presented, in which the hindrance effect resulted from the change o…