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

Analysis of Kinetic Models for Label Switching and Stochastic Gradient Descent