Recent Applications of Dynamical Mean-Field Methods

TL;DR

This paper reviews recent advances in dynamical mean-field methods, highlighting their applications across complex systems like glasses, active matter, ecosystems, neural networks, and quantum systems, emphasizing their predictive capabilities.

Contribution

It summarizes recent developments in dynamical mean-field theory and its diverse applications to complex out-of-equilibrium systems and quantum extensions.

Findings

Captures complex collective dynamics effectively

Provides predictive insights into diverse systems

Enhances understanding of out-of-equilibrium phenomena

Abstract

Rich out of equilibrium collective dynamics of strongly interacting large assemblies emerge in many areas of science. Some intriguing and not fully understood examples are the glassy arrest in atomic, molecular or colloidal systems, flocking in natural or artificial active matter, and the organization and subsistence of ecosystems. The learning process, and ensuing amazing performance, of deep neural networks bears resemblance with some of the before-mentioned examples. Quantum mechanical extensions are also of interest. In exact or approximate manner the evolution of these systems can be expressed in terms of a dynamical mean-field theory which not only captures many of their peculiar effects but also has predictive power. This short review presents a summary of recent developments of this approach with emphasis on applications on the examples mentioned above.