  In this paper we provide a novel approach to the analysis of kinetic models
for label switching, which are used for particle systems that can randomly
switch between gradient flows in different energy landscapes. Besides problems
in biology and physics, we also demonstrate that stochastic gradient descent,
the most popular technique in machine learning, can be understood in this
setting, when considering a time-continuous variant. Our analysis is focusing
on the case of evolution in a collection of external potentials, for which we
provide analytical and numerical results about the evolution as well as the
stationary problem.
