Date: Oct 27, 2014, time: 09:00
Although it has been known for some time that certain gradient-ﬂow structures are related to the large deviations of a stochastic process, until recently we only understood this at the level of examples. In this lecture I will explain a general structure that gives rise to the following property: for every sequence of reversible stochastic processes with a large-deviations principle, the limiting equation is a generalized gradient ﬂow that maps one-to-one to the large-deviations rate function. Therefore the large class of reversible stochastic processes generates a correspondingly large class of generalized gradient ﬂows. This is joint work with Michiel Renger and Alexander Mielke.