23 citations · 24 across the 13 of their papers we have counts for
13 papers
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…
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…
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,…
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…