Deep Spectral Learning of Embedded Latent Transfer Operators for Stochastic Dynamical Systems
We propose a spectral learning method for stochastic nonlinear dynamical systems represented with embedded latent transfer operators in deep feature spaces. We instantiate the method as Deep Spectral Encoder (DSE), an operator-based latent state-space model in which a time-invariant neural encoder implements learnable nonlinear feature maps from observations, and these features define Markovian latent states whose temporal evolution and observation mapping are described by the transfer and observation operators, respectively. Functional canonical correlation analysis in a learnable Galerkin-projected feature space provides state coordinates from past and future observations, and the two l...
| Authors | Ryogo Tanaka, Yoshinobu Kawahara |
|---|---|
| Primary category | cs.LG |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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