Time-reversal symmetry arises naturally as a structural property in many
dynamical systems of interest. While the importance of hard-wiring symmetry is
increasingly recognized in machine learning, to date this has eluded
time-reversibility. In this paper we propose a new neural network architecture
for learning time-reversible dynamical systems from data. We focus in
particular on an adaptation to symplectic systems, because of their importance
in physics-informed learning.