Metric mean dimension of factor maps

TL;DR

This paper explores the metric mean dimension of factor maps, introducing new weighted and relative concepts, establishing variational principles, and applying these to random dynamical systems.

Contribution

It introduces weighted and relative metric mean dimensions for factor maps, along with variational principles and applications to random systems.

Findings

Introduced three types of weighted metric mean dimensions.

Established variational principles for weighted metric mean dimension.

Linked metric mean dimensions of systems via Abramov-Rokhlin formula.

Abstract

Metric mean dimension is a metric-depedent quantity to characterize the topological complexity of systems with infinite topological entropy. In this paper, we investigate metric mean dimension of factor maps. (1) We introduce three types of weighted metric mean dimensions to characterize factor maps with infinite weighted topological entropy, and compare them with the metric mean dimensions of the factor system and the extension system. Furthermore, we establish variational principles for weighted metric mean dimension. (2) We introduce relative conditional metric mean dimension for factor maps with infinite relative topological conditional entropy, and prove that it coincides with relative metric mean dimension. (3) In the context of random dynamical systems, the natural projection from the skew product to its driving system is a one-Lipschitz map. We introduce random average metric mean dimension and use it to establish a topological Abramov-Rokhlin formula for the certain one-Lipschitz map. As an application, we obtain an inequality that links the metric mean dimensions of the driving system, the skew product system, and the inherent non-autonomous dynamical systems from the random dynamical system.