A framework learns effective multiscale stochastic dynamics from single slow-variable paths by parameterizing the fast process invariant distribution with normalizing flows, trained end-to-end via penalized likelihood from stochastic averaging.
Approximate inference for continuous-time Markov processes
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Learning stochastic multiscale models through normalizing flows
A framework learns effective multiscale stochastic dynamics from single slow-variable paths by parameterizing the fast process invariant distribution with normalizing flows, trained end-to-end via penalized likelihood from stochastic averaging.