23 citations · 24 across the 13 of their papers we have counts for
7 papers · 1 filter
Bottomonium suppression with a machine-learning-informed Debye mass
Ajaharul Islam, Shibo Chen, Fu-Peng Li +2
Motivated by recent progress in data-driven approaches, we introduce a machine-learning (ML)-informed Debye mass, extracted from lattice-informed inputs, exclusively in the complex…
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…
Bayesian inference of event-by-event collision geometry from charged-particle multiplicity in heavy-ion collisions
Yige Huang, Fu-Peng Li, Hanwen Feng +1
We propose the Inference-driven Participant Determination (IPD) method, a Bayesian framework for inferring event-by-event posterior distributions of the number of participants ($N_…
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…
Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks
Fu-Peng Li, Long-Gang Pang, Guang-You Qin
The equation of state (EoS) of strongly interacting matter at finite temperature and chemical potentials (baryon, charge, and strangeness) is a crucial input for hydrodynamic simul…
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…