Before revision: We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs . In particular, we introduce a neural-network control (NNC) framework, which represents dynamical systems by neural ordinary different equations (neural ODEs) , and find that NNC can learn control signals that drive networked dynamical systems into desired target states.
After revision: We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs , which we represent with neural ordinary differential equations (neural ODEs) , and find that NNC can learn control signals that drive networked dynamical systems into desired target states.