Grass-roots optimization of coupled oscillator networks

TL;DR

This paper introduces a multiscale 'grass-roots' optimization approach for coupled oscillator networks, demonstrating that local subsystem optimizations can achieve global synchronization comparable to centralized methods, with added robustness.

Contribution

It presents a novel local optimization mechanism for synchronization in coupled oscillators, applicable to biological and physical systems, emphasizing self-organization and robustness.

Findings

Grass-roots optimization achieves synchronization comparable to global methods.

Localized optimization enhances robustness against attacks or subsystem failures.

Applicable to cardiac tissue and power grid models.

Abstract

Despite the prevalence of biological and physical systems for which synchronization is critical, existing theory for optimizing synchrony depends on global information and does not sufficiently explore local mechanisms that enhance synchronization. Thus, there is a lack of understanding for the self-organized, collective processes that aim to optimize/repair synchronous systems, e.g., the dynamics of paracrine signaling within cardiac cells. Here we present ``grass-roots'' optimization of synchronization, which is a multiscale mechanism in which local optimizations of smaller subsystems cooperate to collectively optimize an entire system. Considering models of cardiac tissue and a power grid, we show that grass-roots-optimized systems are comparable to globally optimized systems, but they also have the added benefit of being robust to targeted attacks or subsystem islanding. Our findings motivate and support further investigation into the physics of local mechanisms that can support self-optimization for complex systems.