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.
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A variational method learns a neural approximation to the conditional backward-in-time score of the posterior SDE, inducing an ELBO for joint smoothing and parameter learning from sparse data.
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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.
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Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows
A variational method learns a neural approximation to the conditional backward-in-time score of the posterior SDE, inducing an ELBO for joint smoothing and parameter learning from sparse data.