SSA and DA extract barrier-sensitive mode separation from the autocovariance matrix of a unique constant-coefficient diffusion with the given density as stationary distribution.
Pavliotis and Andrew M
2 Pith papers cite this work. Polarity classification is still indexing.
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Tangent-bundle and inverse-consistency penalties derived from observed covariance improve autoencoder learning of nonlinear charts and latent SDEs, reducing radial mean first-passage time errors by 50-70% on embedded surfaces.
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Measuring and Decomposing Mode Separation via the Canonical Diffusion
SSA and DA extract barrier-sensitive mode separation from the autocovariance matrix of a unique constant-coefficient diffusion with the given density as stationary distribution.
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Geometric regularization of autoencoders via observed stochastic dynamics
Tangent-bundle and inverse-consistency penalties derived from observed covariance improve autoencoder learning of nonlinear charts and latent SDEs, reducing radial mean first-passage time errors by 50-70% on embedded surfaces.