Renormalization Group

Renormalization group (RG) theory, a powerful tool for analyzing complex systems by systematically integrating out irrelevant details, is finding increasing application beyond its traditional physics domain. Current research focuses on applying RG principles to diverse areas, including deep neural networks (where RG helps understand training dynamics and feature learning), generative adversarial networks (improving stability and performance with limited data), and graph neural networks (enhancing performance through graph rewiring). This interdisciplinary approach promises to improve model understanding, efficiency, and generalization across various fields, from predictive maintenance to quantum many-body physics.