J. D. Baker, C. A. Bertulani, R. V. Lobato

We present a physics-informed Bayesian neural-network framework to infer neutron-star equations of state from theoretical priors and to propagate the associated uncertainties to stellar observables. Trained on a large and representative ensemble of hadronic EoSs, the model learns $P(ε)$ via stochastic variational inference, incorporating soft constraints at saturation density and from perturbative QCD, together with penalties enforcing monotonicity and causality. The accepted core EoSs are matched to an SLy4 crust and evolved through a unified Tolman-Oppenheimer-Volkoff-plus-tidal solver to generate posterior predictions in the mass-radius ($M$-$R$) and mass-tidal-deformability ($M$-$Λ$) planes. The inferred posterior is consistent with NICER radius measurements and the observed $2.0\,M_\odot$ maximum-mass constraint, yielding $R_{1.4}=12.1^{+1.4}_{-0.9}\,\mathrm{km}$, $Λ_{1.4}=580^{+520}_{-240}$, and $M_{\mathrm{max}}\simeq 2.11\pm0.05\,M_\odot$ (90\% CI). The resulting canonical tidal deformability can be assessed \emph{a posteriori} against current gravitational-wave constraints. Overall, this framework provides a flexible, non-parametric mapping from microphysical EoS uncertainties to neutron-star observables.