  Boltzmann generators approach the sampling problem in many-body physics by
combining a normalizing flow and a statistical reweighting method to generate
samples in thermodynamic equilibrium. The equilibrium distribution is usually
defined by an energy function and a thermodynamic state. Here we propose
temperature-steerable flows (TSF) which are able to generate a family of
probability densities parametrized by a choosable temperature parameter. TSFs
can be embedded in generalized ensemble sampling frameworks to sample a
physical system across multiple thermodynamic states.
