54 citations · 125 across the 9 of their papers we have counts for
5 papers · 1 filter
Advancing Nuclear Physics with Machine Learning and Artificial Intelligence
Wanbing He, Qingfeng Li, Yugang Ma +3
Machine learning (ML) and artificial intelligence (AI) are becoming powerful tools in scientific research across various disciplines. In this review, we summarize recent progress i…
Neural-network excited states of 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…
Machine learning the single- hypernuclei with neural-network quantum states
Zi-Xiao Zhang, Yi-Long Yang, Wan-Bing He +3
Single- hypernuclei are the most straightforward extension of atomic nuclei. A thorough description of baryonic system beyond first-generation quark sector is indispensable for…
Nuclear liquid-gas phase transition with machine learning
Rui Wang, Yu-Gang Ma, R. Wada +4
The machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ the machine-learning techniques to study the…
Collective flows of O + O collisions with -clustering configurations
Chen-Chen Guo, Wan-Bing He, Yu-Gang Ma
The main purpose of the present paper is to discuss whether or not the collective flows in heavy-ion collision at Fermi energy can be taken as a tool to investigate the cluster con…