  We present AI Poincar\'e, a machine learning algorithm for auto-discovering
conserved quantities using trajectory data from unknown dynamical systems. We
test it on five Hamiltonian systems, including the gravitational 3-body
problem, and find that it discovers not only all exactly conserved quantities,
but also periodic orbits, phase transitions and breakdown timescales for
approximate conservation laws.
