Mean Field

Mean field theory provides a powerful framework for analyzing and approximating the behavior of large systems of interacting entities, simplifying complex interactions by considering the average effect of the population. Current research focuses on applying mean field approaches to diverse problems, including solving partial differential equations, optimizing control strategies in multi-agent systems (like mean field games and control), and improving the efficiency of machine learning algorithms (e.g., through variational inference and neural network training). These advancements have significant implications for various fields, enabling more efficient solutions to complex problems in areas such as energy markets, traffic flow prediction, and large language model training.