Research on dynamical systems has led to the development of new theories and methods for understanding and controlling complex networks. These advancements include a symbolic network-based nonlinear theory and various control strategies for multi-dimensional systems. The exact nature and applications of these developments are not entirely clear, but they appear to involve model-based reinforcement learning, dynamic inversion, and optimization control.