23 citations · 24 across the 13 of their papers we have counts for
6 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…
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…
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…
Neural Network Modeling of Heavy-Quark Potential from Holography
Ou-Yang Luo, Xun Chen, Fu-Peng Li +2
Using Multi-Layer Perceptrons (MLP) and Kolmogorov-Arnold Networks (KAN), we construct a holographic model based on lattice QCD data for the heavy-quark potential in the 2+1 system…
Deep-learning quasi-particle masses from QCD equation of state
Fu-Peng Li, Hong-Liang Lü, Long-Gang Pang +1
The interactions of quarks and gluons are strong at non-perturbative region. The equation of state (EoS) of a strongly-interacting quantum chromodynamics (QCD) medium can only be s…