  We introduce a Bayesian protocol based on artificial neural networks that is
suitable for modeling inclusive electron-nucleus scattering on a variety of
nuclear targets with quantified uncertainties. Unlike previous applications in
the field, which directly parameterize the cross sections, our approach employs
artificial neural networks to represent the longitudinal and transverse
response functions. In contrast to cross sections, which depend on the incoming
energy, scattering angle, and energy transfer, the response functions are
determined solely by the energy and momentum transfer to the system, allowing
the angular component to be treated analytically. We assess the accuracy and
predictive power of our framework against the extensive data in the
quasielastic inclusive electron-scattering database. Additionally, we present
novel extractions of the longitudinal and transverse response functions and
compare them with previous experimental analysis and nuclear ab-initio
calculations.
