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We present a method to learn mean residence time and escape probability fromdata modeled by stochastic differential equations. This method is a combinationof machine learning from data (to extract stochastic differential equations asmodels) and stochastic dynamics (to quantify dynamical behaviors withdeterministic tools). The goal is to learn and understand stochastic dynamicsbased on data. This method is applicable to sample path data collected fromcomplex systems, as long as these systems can be modeled as stochasticdifferential equations.

Discovering mean residence time and escape probability from data of stochastic dynamical systems