Determining the NJL Coupling and AMM in Magnetized QCD Matter via Machine Learning
This study employs a physics-informed machine learning framework to determine field-dependent Nambu-Jona-Lasinio model parameters, specifically the running coupling constant and quark anomalous magnetic moment, by fitting lattice QCD data to successfully reproduce the inverse magnetic catalysis effect in magnetized QCD matter.