  An efficient representation of observed data has many benefits in various
domains of engineering and science. Representing static data sets, such as
images, is a living branch in machine learning and eases downstream tasks, such
as classification, regression, or decision making. However, the representation
of dynamical systems has received less attention. In this work, we develop a
method to represent a dynamical system efficiently as a combination of a state
and a local model, which fulfills a criterion inspired by the minimum
description length (MDL) principle. The MDL principle is used in machine
learning and statistics to quantify the trade-off between the ability to
explain seen data and the model complexity. Networked control systems are a
prominent example, where such a representation is beneficial. When many agents
share a network, information exchange is costly and should thus happen only
when necessary. We empirically show the efficiency of the proposed encoding for
several dynamical systems and demonstrate reduced communication for
event-triggered state estimation problems.
