  In the framework of three-active-neutrino mixing, the charge parity phase,
the neutrino mass ordering, and the octant of $\theta_{23}$ remain unknown. The
Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline
neutrino oscillation experiment, which aims to address these questions by
measuring the oscillation patterns of $\nu_\mu/\nu_e$ and
$\bar\nu_\mu/\bar\nu_e$ over a range of energies spanning the first and second
oscillation maxima. DUNE far detector modules are based on liquid argon TPC
(LArTPC) technology. A LArTPC offers excellent spatial resolution, high
neutrino detection efficiency, and superb background rejection, while
reconstruction in LArTPC is challenging. Deep learning methods, in particular,
Convolutional Neural Networks (CNNs), have demonstrated success in
classification problems such as particle identification in DUNE and other
neutrino experiments. However, reconstruction of neutrino energy and final
state particle momenta with deep learning methods is yet to be developed for a
full AI-based reconstruction chain. To precisely reconstruct these kinematic
characteristics of detected interactions at DUNE, we have developed and will
present two CNN-based methods, 2-D and 3-D, for the reconstruction of final
state particle direction and energy, as well as neutrino energy. Combining
particle masses with the kinetic energy and the direction reconstructed by our
work, the four-momentum of final state particles can be obtained. Our models
show considerable improvements compared to the traditional methods for both
scenarios.
