AI & ML
Dynamics Aware Past-Future Information Bottleneck
arXiv eess.SY0 views
arXiv:2611.08426v1 Announce Type: cross Abstract: The simplification of complex dynamical systems is essential for efficient estimation and control. A principled approach to this task is through the lens of information-theoretic compression. The Past-Future Information Bottleneck (PF-IB) provides a theoretical framework for compressing linear dynamical systems. However, the standard formulation often lacks structural consistency, yielding representations that are not realizable as dynamical systems. By constraining the objective to a specific manifold of block Hankel matrices, which we prove is a Lie group, we bridge the gap between PF-IB and realization theory. Our approach ensures that the resulting compressed dynamics are realizable, thereby enabling its direct application to estimation and control. We demonstrate our approach in numerical simulations.
Source:https://arxiv.org/abs/2611.08426PreviousA Multimodal Haptic Interface for Simultaneous Softness and Thermal ModulationNextModeling Rainwater Harvesting Systems with Covered Storage Tank on A Smartphone
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