  We exploit the great potential offered by Bayesian Neural Networks (BNNs) to
directly decipher the internal composition of neutron stars (NSs) based on
their macroscopic properties. By analyzing a set of simulated observations,
namely NS radius and tidal deformability, we leverage BNNs as effective tools
for inferring the proton fraction and sound speed within NS interiors. To
achieve this, several BNNs models were developed upon a dataset of $\sim$ 25K
nuclear EoS within a relativistic mean-field framework, obtained through
Bayesian inference that adheres to minimal low-density constraints. Unlike
conventional neural networks, BNNs possess an exceptional quality: they provide
a prediction uncertainty measure. To simulate the inherent imperfections
present in real-world observations, we have generated four distinct training
and testing datasets that replicate specific observational uncertainties. Our
initial results demonstrate that BNNs successfully recover the composition with
reasonable levels of uncertainty. Furthermore, using mock data prepared with
the DD2, a different class of relativistic mean-field model utilized during
training, the BNN model effectively retrieves the proton fraction and speed of
sound for neutron star matter.
