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.
On the capacity of deep generative networks for approximating distributions.Neural networks, 145:144–154
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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.