Quantifying order outside of equilibrium

The analysis of computable information density (CID) has recently been introduced as a general approach to quantifying order in equilibrium and non-equilibrium many-body systems, both discrete and continuous, even when the underlying form of order is unknown.

The approach has been shown to reliably identify phase transitions, determine their character without prior knowledge of the order parameters. 

A natural question is whether CID may also inform us on the existence of diverging correlation lengths, and their exponents, in the proximity of phase transitions.

I explore methods to compute the CID, use it to quantify phase transition for new models, study its flow under a renormalization group transformations and show how this can be exploited to extract correlation lengths