1 citations · 1 across the 3 of their papers we have counts for
6 papers
Systematic study of one-point kinetic energy density functionals for atomic nuclei
Tian Shuai Shang, Jian Li, Haozhao Liang +5
To explore the applicability of orbital-free density functional theory (OF-DFT) in nuclear physics, we perform a systematic benchmark of 36 one-point kinetic energy density functio…
Predictions of charge density distributions for nuclei with
Yun Dong Wang, Tian Shuai Shang, Hui Hui Xie +3
A deep neural network (DNN) has been developed to accurately predict nuclear charge density distributions for nuclei with proton numbers . By incorporating essential nucl…
Bridging Theory and Data: Correcting Nuclear Mass Models with Interpretable Machine Learning
Yanhua Lu, Tianshuai Shang, Pengxiang Du +2
Nuclear mass prediction is one of the core issues in nuclear physics research, yet it faces the challenge of small-sample datasets with high complexity. This study introduces the K…
Nuclear mass predictions based on convolutional neural network
Yanhua Lu, Tianshuai Shang, Pengxiang Du +3
A convolutional neural network (CNN) is employed to investigate nuclear mass. By introducing the masses of neighboring nuclei and the paring effects at the input layer of the netwo…
Nuclear mass table in deformed relativistic Hartree-Bogoliubov theory in continuum, II: Even- nuclei
DRHBc Mass Table Collaboration, Peng Guo, Xiaojie Cao +80
The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even- nuclei with $8\le Z\…
Global prediction of nuclear charge density distributions using deep neural network
Tian Shuai Shang, Hui Hui Xie, Jian Li +1
A deep neural network (DNN) has been developed to generate the distributions of nuclear charge density, utilizing the training data from the relativistic density functional theory…