  Stochastic normalizing flows are a class of deep generative models that
combine normalizing flows with Monte Carlo updates and can be used in lattice
field theory to sample from Boltzmann distributions. In this proceeding, we
outline the construction of these hybrid algorithms, pointing out that the
theoretical background can be related to Jarzynski's equality, a
non-equilibrium statistical mechanics theorem that has been successfully used
to compute free energy in lattice field theory. We conclude with examples of
applications to the two-dimensional $\phi^4$ field theory.
