Recent Applications of Dynamical Mean-Field Methods

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 that 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.