119 citations · 159 across the 5 of their papers we have counts for
5 papers
Solving Schrodinger equations using physically constrained neural network
Kai-Fang Pu, Hanlin Li, Hong-Liang Lu +1
Deep neural network (DNN) and auto differentiation have been widely used in computational physics to solve variational problems. When DNN is used to represent the wave function to…
Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning
Yongjia Wang, Zepeng Gao, Hongliang Lü +1
Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectat…
Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
Yongjia Wang, Fupeng Li, Qingfeng Li +2
A deep convolutional neural network (CNN) is developed to study symmetry energy effects by learning the mapping between the symmetry energy and the two-dimensional…
Machine learning the nuclear mass
Zepeng Gao, Yongjia Wang, Hongliang Lü +3
Background: The masses of about 2500 nuclei have been measured experimentally, however more than 7000 isotopes are predicted to exist in the nuclear landscape from H (Z=1) to Og (Z…
Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
Fupeng Li, Yongjia Wang, Hongliang Lü +3
The impact parameter is one of the crucial physical quantities of heavy-ion collisions (HICs), and can affect obviously many observables at the final state, such as the multifragme…