Network Control

Network control research focuses on designing and optimizing algorithms to manage complex systems, from traffic flow and communication networks to spacecraft guidance and personalized medicine. Current efforts leverage machine learning, particularly reinforcement learning and graph neural networks, often incorporating novel architectures like neural ODEs and differentiable discrete event simulation to improve efficiency and scalability. These advancements aim to create more robust, adaptable, and efficient control systems across diverse applications, impacting fields ranging from transportation and telecommunications to healthcare and aerospace engineering.