  As children grow older, they develop an intuitive understanding of the
physical processes around them. Their physical understanding develops in
stages, moving along developmental trajectories which have been mapped out
extensively in previous empirical research. Here, we investigate how the
learning trajectories of deep generative neural networks compare to children's
developmental trajectories using physical understanding as a testbed. We
outline an approach that allows us to examine two distinct hypotheses of human
development - stochastic optimization and complexity increase. We find that
while our models are able to accurately predict a number of physical processes,
their learning trajectories under both hypotheses do not follow the
developmental trajectories of children.
