aiqbench analysis
DySIB introduces a novel framework for inferring low-dimensional dynamical state variables directly from high-dimensional time-series data by optimizing predictive mutual information in latent space. By avoiding explicit observation reconstruction, this method successfully recovers interpretable physical coordinates like angle and velocity from experimental pendulum data without manual supervision. This approach offers a significant step forward for physical scientists seeking to extract meaningful state representations from complex, noisy datasets.
Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
arXiv:2604.24662v2 Announce Type: replace-cross Abstract: 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 of a physical pendulum, where the underlying state space is known. The method, with hyperparameters of the learning architecture set self-consistently by the data, recovers a two-dimensional representation that matches the dimensionality, topology, and geometry of the pendulum phase space, with the learned coordinates aligning smoothly with the canonical angle and angular velocity. These results demonstrate, on a well-characterized experimental system, that predictive information in latent space can be used to recover interpretable dynamical coordinates directly from high-dimensional data.
Abstract: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 of a physical pendulum, where the underlying state space is known. The method, with hyperparameters of the learning architecture set self-consistently by the data, recovers a two-dimensional representation that matches the dimensionality, topology, and geometry of the pendulum phase space, with the learned coordinates aligning smoothly with the canonical angle and angular velocity. These results demonstrate, on a well-characterized experimental system, that predictive information in latent space can be used to recover interpretable dynamical coordinates directly from high-dimensional data.
Data Analysis, Statistics and Probability (physics.data-an); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
arXiv:2604.24662 [physics.data-an]
(or arXiv:2604.24662v2 [physics.data-an] for this version)
https://doi.org/10.48550/arXiv.2604.24662 Focus to learn more arXiv-issued DOI via DataCite
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