  Many important physical processes have dynamics that are too complex to
completely model analytically. Optimisation of such processes often relies on
intuition, trial-and-error, or the construction of empirical models. Machine
learning based on artificial neural networks has emerged as an efficient means
to develop empirical models of complex systems. We implement a deep artificial
neural network to optimise the magneto-optic cooling and trapping of neutral
atomic ensembles. Cold atomic ensembles have become commonplace in laboratories
around the world, however, many-body interactions give rise to complex dynamics
that preclude precise analytic optimisation of the cooling and trapping
process. The solution identified by machine learning is radically different to
the smoothly varying adiabatic solutions currently used. Despite this, the
solutions vastly outperform best known solutions producing higher optical
densities. This may provide a pathway to a new understanding of the dynamics of
the cooling and trapping processes in cold atomic ensembles.
