Synchronization control in networked dynamical systems requires regulating not only whether coherence is achieved but also when and to what extent it emerges. We propose a physics-informed neural network framework for continuous-time synchronization regulation, in which system trajectories and control inputs are jointly parameterized and constrained by the governing dynamics. Macroscopic synchronization objectives are imposed directly at the trajectory level by enforcing persistence conditions on the order parameter after a prescribed target time. This formulation enables simultaneous control of synchronization time and coherence level without assuming any explicit feedback law or solving a strict optimal control problem. Numerical studies on networked Kuramoto oscillators demonstrate smooth synchronization with reduced transient control effort and competitive cumulative cost relative to analytical baselines. The framework remains effective in nongradient and frustrated dynamics, highlighting physics-informed neural control as a flexible trajectory-level approach to synchronization regulation. This perspective enables controlled access to nontrivial collective states, including chimera and chaotic dynamics.

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• Synchronization
• Artificial neural networks
• Collective dynamics
• Machine learning