15 citations · 25 across the 22 of their papers we have counts for
43 papers · 1 filter
Vector-meson properties in a data-driven AdS/QCD model
Xun Chen, Floriana Giannuzzi, Stefano Nicotri
We investigate the properties of , , and mesons within a holographic AdS/QCD framework, capturing both their vacuum phenomenology and in-medium dynamics. By reconstructin…
Lattice-data-driven specific heat and isentropic bulk modulus of SU(3) gluon matter at finite temperature
Wei Shen, Zhen-Yan Lu, Muhammad Waqas +3
We investigate the specific heat and isentropic bulk modulus of finite-temperature pure SU(3) gauge matter within a lattice-data-driven phenomenological framework. The equation of…
Critical behavior and critical exponents of rotating QCD matter
Kai Xiao, Fei Sun, Shuang Li +1
We investigate the thermodynamic properties and critical behavior of rotating strongly interacting matter within the two-flavor Nambu--Jona-Lasinio (NJL) model in the mean-field ap…
Neural-Network Holographic Model of the QCD Phase Transition under Lattice and HRG Constraints
De-Xing Zhu, Li-Qiang Zhu, Xun Chen +2
Within a neural-network-based holographic framework, we incorporate lattice QCD (LQCD) and Hadron Resonance Gas (HRG) data to train the model and predict the location of the QCD cr…
Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model
Yu Zhang, Xun Chen, Miguel Angel Martin Contreras
We present a data-driven inverse construction of the dilaton field in a bottom-up AdS/QCD description of heavy vector quarkonia. Instead of adopting an \emph{ad hoc} analytic ansat…
Discovering the Gell-Mann-Okubo Formula with Kolmogorov-Arnold Networks
Jian-Yao He, Xun Chen, Xiao-Yan Zhu +1
Uncovering physical laws from experimental data is a fundamental goal of theoretical physics. In this work, we apply the spline-based, interpretable Kolmogorov-Arnold Network (KAN)…