  The dynamics of physical systems is often constrained to lower dimensional
sub-spaces due to the presence of conserved quantities. Here we propose a
method to learn and exploit such symmetry constraints building upon Hamiltonian
Neural Networks. By enforcing cyclic coordinates with appropriate loss
functions, we find that we can achieve improved accuracy on simple classical
dynamics tasks. By fitting analytic formulae to the latent variables in our
network we recover that our networks are utilizing conserved quantities such as
(angular) momentum.
