  In the study of closed many-body quantum systems one is often interested in
the evolution of a subset of degrees of freedom. On many occasions it is
possible to approach the problem by performing an appropriate decomposition
into a bath and a system. In the simplest case the evolution of the reduced
state of the system is governed by a quantum master equation with a
time-independent, i.e. Markovian, generator. Such evolution is typically
emerging under the assumption of a weak coupling between the system and an
infinitely large bath. Here, we are interested in understanding to which extent
a neural network function approximator can predict open quantum dynamics -
described by time-local generators - from an underlying unitary dynamics. We
investigate this question using a class of spin models, which is inspired by
recent experimental setups. We find that indeed time-local generators can be
learned. In certain situations they are even time-independent and allow to
extrapolate the dynamics to unseen times. This might be useful for situations
in which experiments or numerical simulations do not allow to capture long-time
dynamics and for exploring thermalization occurring in closed quantum systems.
