  This paper introduces a new approach to reconstruct cosmological functions
using artificial neural networks based on observational measurements with
minimal theoretical and statistical assumptions. By using neural networks, we
can generate computational models of observational datasets, and then we
compare them with the original ones to verify the consistency of our method.
This methodology is applicable to even small-size datasets. In particular, we
test the proposed method with data coming from cosmic chronometers, $f\sigma_8$
measurements, and the distance modulus of the Type Ia supernovae. Furthermore,
we introduce a first approach to generate synthetic covariance matrices through
a variational autoencoder, using the systematic covariance matrix of the Type
Ia supernova compilation.
