  We investigate whether a neural network approach can reproduce and predict
the electron-nucleus cross sections in the kinematical domain of present and
future accelerator-based neutrino oscillation experiments. For this purpose, we
consider the large amount of data available to the community via the web-page
``Quasielastic Electron Nucleus scattering archive'', and use a residual, fully
connected feedforward neural network. We illustrate the training performances
of the neural network by comparing its results with experimental data for the
electron double-differential cross section on carbon. The agreement between
predictions and data is remarkable from quasielastic to deep-inelastic
scattering. To test the predicting power of the neural network we consider the
numerous kinematical conditions for which experimental cross sections on
calcium are available. Furthermore, we show the predictions of the electron
scattering cross sections on oxygen, argon, and titanium: nuclei of particular
interest in the context of present and future accelerator-based neutrino
oscillation program. The agreement between these predictions and the data is
comparable to the one of other theoretical models commonly used to calculate
electron and neutrino cross sections, such as SuSAv2 and GiBUU. Results
obtained with GENIE, a Monte Carlo event generator, are also discussed for
comparison. The good performances obtained with our neural network suggest that
neural networks could be exploited for theoretical and experimental
investigations of electron- and neutrino-nucleus scattering.
