53 citations · 62 across the 14 of their papers we have counts for
7 papers · 1 filter
Hadronic rescattering effects on net-proton cumulants from functional renormalization group calculations
Qianru Lin, Shi Yin, Jianing Li +4
Net-proton cumulants in the Beam Energy Scan region of heavy-ion collisions are widely used to probe critical fluctuations associated with the conjectured critical endpoint of Quan…
Neural network maximum entropy framework for distribution reconstruction in heavy-ion collisions
Qian-Ru Lin, Fu-Peng Li, YiGe Huang +1
We develop a neural-network maximum-entropy (NN+MaxEnt) framework for reconstructing probability distributions from limited observables in heavy-ion collisions. The method combines…
CLVisc Agent for autonomous relativistic hydrodynamics studies
Qi Wang, Long-Gang Pang, Shi Pu +1
We enable large language model (LLM) agents to autonomously perform end-to-end hydrodynamic simulations of the quark-gluon plasma evolution and calculation of final hadron spectra…
Global polarization of hyperons and its sensitivity to equations of state in low-energy heavy-ion collisions
Cong Yi, Shi Pu, Long-Gang Pang +2
Significant global polarization of hyperons along the direction of the orbital angular momentum has been measured in non-central heavy-ion collisions where the equation of stat…
Nuclear equation of state at finite using deep learning assisted quasi-parton model
Fu-Peng Li, Long-Gang Pang, Guang-You Qin
To accurately determine the nuclear equation of state (EoS) at finite baryon chemical potential () remains a challenging yet essential goal in the study of QCD matter under ex…
Phase Transition Study meets Machine Learning
Yu-Gang Ma, Long-Gang Pang, Rui Wang +1
In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of ph…
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…