53 citations · 62 across the 14 of their papers we have counts for
15 papers
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…
Unbiased Data-Driven Determination of the Nuclear Dipole Amplitude in the Color Glass Condensate
Si-Wei Dai, Haowu Duan, Long-Gang Pang +4
Gluon saturation limits the growth of parton densities at small Bjorken- and is expected to be most pronounced in heavy nuclei. Yet quantitative extractions of the nuclear gluon…
Physics-Informed Neural Network with Squeeze-Excitation-like Attention
Yun-Fei Song, Long-Gang Pang, Fu-Peng Li +1
We introduce SEA-PINN, a novel architecture that incorporates a Squeeze-Excitation-like attention mechanism into physics-informed neural networks to dynamically recalibrate the imp…
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…
Physics-Informed Global Extraction of the Universal Small- Dipole Amplitude
Si-Wei Dai, Fu-Peng Li, Long-Gang Pang +4
We extract the universal small- dipole scattering amplitude from a global analysis based on a physics-informed neural network (PINN), without imposing a priori MV-typ…
Parton Fragmentation Functions Extracted with a Physics-Informed Neural Network
Si-Wei Dai, Fu-Peng Li, Long-Gang Pang +3
Reliable predictions of many high-energy strong interaction processes rely heavily on the non-perturbative parton fragmentation functions (FFs) extracted from existing experimental…
Phase-space entropy at acquisition reflects downstream learnability
Xiu-Cheng Wang, Jun-Jie Zhanga, Nan Cheng +3
Modern learning systems work with data that vary widely across domains, but they all ultimately depend on how much structure is already present in the measurements before any model…
Melting of heavy quarkonia in QGP using deep neural networks
Mohammad Yousuf Jamal, Fu-Peng Li, Long-Gang Pang +1
Machine learning techniques have emerged as powerful tools for tackling non-perturbative challenges in quantum chromodynamics. In this study, we introduce a data-driven framework e…
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…
Is AI Robust Enough for Scientific Research?
Jun-Jie Zhang, Jiahao Song, Xiu-Cheng Wang +14
We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant d…
Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion
Jun-Jie Zhang, Nan Cheng, Fu-Peng Li +4
Understanding the mechanisms behind neural network optimization is crucial for improving network design and performance. While various optimization techniques have been developed,…
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…