Noise-induced synchronization is a pervasive phenomenon observed in a multitude of natural and engineering systems. Here, we devise a machine learning framework with the aim of devising noise controllers to achieve synchronization in diverse complex physical systems. We find the implicit energy regularization phenomenon of the formulated framework that engenders energy-saving artificial noise and we rigorously elucidate the underlying mechanism driving this phenomenon. We substantiate the practical feasibility and efficacy of this framework by testing it across various representative systems of physical and biological significance, each influenced by distinct constraints reflecting real-world scenarios.

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• Control & applications of chaos
• Dynamics of networks
• Network stability
• Noise-induced transitions
• Synchronization transition
• Artificial neural networks
• Real world networks
• Stochastic networks
• Collective behavior models
• Machine learning