Quantifying statistical mechanical learning in a many-body system with machine learning

Far-from-equilibrium many-body systems, from soap bubbles to suspensions to polymers, learn the drives that push them. This learning has been characterized with thermodynamic properties, such as work dissipation and strain. We move beyond these macroscopic properties first defined for equilibrium contexts: We quantify statistical mechanical learning with machine learning. Our strategy relies on a parallel that we identify between representation learning and statistical mechanics in the presence of a drive. We apply this parallel to measure novelty detection, classification, and memory capacity. Numerical simulations of a spin glass illustrate our technique. This toolkit exposes self-organization that eludes detection by thermodynamic measures. Our toolkit more reliably and precisely identifies and quantifies learning by matter.

https://arxiv.org/abs/2001.03623