  The difficulty in describing the equation of state (EoS) for nuclear matter
at densities above the saturation density ($\rho_0$) has led to the emergence
of a multitude of models based on different assumptions and techniques. These
EoSs, when used to describe a neutron star (NS), lead to differing values of
observables. An outstanding goal in astrophysics is to constrain the dense
matter EoS by exploiting astrophysical and gravitational wave measurements.
Nuclear matter parameters appear as Taylor coefficients in the expansion of the
EoS around the saturation density of symmetric and asymmetric nuclear matter
and provide a physically-motivated representation of the EoS. In this paper, we
introduce a deep learning-based methodology to predict key neutron stars
observables such as the NS mass, NS radius, and tidal deformability from a set
of nuclear matter parameters. Using generated mock data, we confirm that the
neural network model is able to accurately capture the underlying physics of
finite nuclei and replicate inter-correlations between the symmetry energy
slope, its curvature, and the tidal deformability arising from a set of
physical constraints. We also test our network with mock data generated by a
different class of physics model, which was not part of the training, to
explore the limitations of model dependency in the results. We also study the
validity of our trained model using Bayesian inference and show that the
performance of our model is on par with physics-based models with the added
benefit of much lower computational cost.
