Adversarial control of synchronization in complex oscillator networks

Area of Science:

• Complex Systems
• Network Science
• Dynamical Systems Theory
• Artificial Intelligence

Background:

• Controlling synchronization in complex networks is crucial for understanding phenomena in physics, biology, and engineering.
• Traditional methods often require significant energy input or global network information.
• Adversarial attack principles from deep learning offer a new perspective for targeted system manipulation.

Purpose of the Study:

• To develop a novel perturbation strategy for controlling synchronization dynamics in Kuramoto oscillator networks.
• To leverage deep learning concepts, specifically adversarial attacks, for precise synchronization management.
• To investigate the effectiveness of small phase perturbations in enhancing or suppressing collective synchronization.

Main Methods:

• Formulation of synchronization control as a gradient-based optimization problem.
• Computation of gradients of the order parameter with respect to individual oscillator phases.
• Identification of optimal small phase perturbations to steer network synchronization.

Main Results:

• Demonstrated that extremely small phase perturbations can achieve significant synchronization control across diverse network architectures.
• Synchronization enhancement is effective across various network sizes.
• Synchronization suppression is particularly effective in larger networks, with scalability observed.

Conclusions:

• The proposed adversarial framework provides a novel paradigm for synchronization management in networked dynamical systems.
• Deep learning-inspired perturbation strategies offer an efficient method for controlling collective behavior.
• The approach is validated on various canonical and real-world network topologies, including power grids and brain networks.