Controlling synchronization dynamics via physics-informed neural networks

TL;DR

This paper introduces a physics-informed neural network framework for controlling synchronization in networked dynamical systems, enabling precise regulation of synchronization timing and coherence without explicit feedback laws.

Contribution

It presents a novel PINN-based approach for continuous-time synchronization control that directly enforces dynamics and objectives at the trajectory level.

Findings

Achieves smooth synchronization with reduced control effort

Demonstrates effectiveness in non-gradient and frustrated dynamics

Outperforms analytical baselines in numerical studies

Abstract

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 (PINN) 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 non-gradient and frustrated dynamics, highlighting physics-informed neural control as a flexible trajectory-level approach to synchronization regulation.