  Faithful energy reconstruction is foundational for precision neutrino
experiments like DUNE, but is hindered by uncertainties in our understanding of
neutrino--nucleus interactions. Here, we demonstrate that dense neural networks
are very effective in overcoming these uncertainties by estimating inaccessible
kinematic variables based on the observable part of the final state. We find
improvements in the energy resolution by up to a factor of two compared to
conventional reconstruction algorithms, which translates into an improved
physics performance equivalent to a 10-30% increase in the exposure.
