Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
Identifying the dynamical state variables of a system from high-dimensional observations is a central problem across physical sciences. The challenge is that the state variables are not directly observable and must be inferred from raw high-dimensional data without supervision. Here we introduce DySIB (Dynamical Symmetric Information Bottleneck) as a method to learn low-dimensional representations of time-series data by maximizing predictive mutual information between past and future observation windows while penalizing representation complexity. This objective operates entirely in latent space and avoids reconstruction of the observations. We apply DySIB to an experimental video dataset ...
| Authors | K. Michael Martini, Eslam Abdelaleem, Paarth Gulati et al. |
|---|---|
| Primary category | physics.data-an |
| Published | 27 April 2026 |
| Last updated at source | 10 June 2026 |
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