Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories
Mikhail Genkin,
Owen Hughes,
Tatiana A. Engel
Abstract:Many complex systems operating far from the equilibrium exhibit stochastic dynamics that can be described by a Langevin equation. Inferring Langevin equations from data can reveal how transient dynamics of such systems give rise to their function. However, dynamics are often inaccessible directly and can be only gleaned through a stochastic observation process, which makes the inference challenging. Here we present a nonparametric framework for inferring the Langevin equation, which explicitly models the stoch… Show more
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