Yilong Yang

activity

20222026

most citedDeep-neural-network approach to solving the ab initio nuclear structure problem

31 citations · 59 across the 11 of their papers we have counts for

collaborators

Showing 2026Show all

3 papers · 1 filter

nucl-th2026

Emergence of the halo in $^{11}$ Li from full nuclear many-body dynamics

Yilong Yang, Pengwei Zhao

The two-neutron halo nucleus $^{11}$ Li is a paradigmatic quantum many-body system whose large spatial extent and weak binding have long challenged a microscopic description from fi…

nucl-th2026

Efficient emulation of nuclear ground states with neural-network variational Monte Carlo and eigenvector continuation

Mao Li, Yilong Yang, Pengwei Zhao

An efficient emulator for \emph{ab initio} calculations of nuclear ground-state properties is developed by integrating the neural-network variational Monte Carlo framework, Feynman…

nucl-th2026

Neural-network excited states of $A = 4$ nuclei and hypernuclei

Zi-Xiao Zhang, Yi-Long Yang, Xiao-Lu Qian +4

We present the first variational Monte Carlo study of nuclear and hypernuclear excited states within the neural-network quantum states (NQS) framework. We implement both the overla…

Emergence of the halo in 11Li from full nuclear many-body dynamics

Efficient emulation of nuclear ground states with neural-network variational Monte Carlo and eigenvector continuation

Neural-network excited states of A=4 nuclei and hypernuclei

Emergence of the halo in $^{11}$ Li from full nuclear many-body dynamics

Neural-network excited states of $A = 4$ nuclei and hypernuclei