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Related Experiment Video

Updated: Jul 6, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Synchronization in the Kuramoto model: a dynamical gradient network approach.

Maoyin Chen1, Yun Shang, Yong Zou

  • 1Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 21, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamical gradient network to achieve synchronization in the Kuramoto model. The method effectively synchronizes oscillators, even with delayed couplings and external noise.

Related Experiment Videos

Last Updated: Jul 6, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Complex systems
  • Network science
  • Nonlinear dynamics

Background:

  • The Kuramoto model is a fundamental framework for studying synchronization phenomena in coupled oscillator systems.
  • Achieving robust synchronization, especially in the presence of network dynamics and external perturbations, remains a significant challenge.

Purpose of the Study:

  • To propose and validate a novel dynamical gradient network approach for adaptive coupling adjustment in the Kuramoto model.
  • To demonstrate the effectiveness of the proposed method in achieving oscillator synchronization under various conditions, including delayed couplings and external noise.

Main Methods:

  • A dynamical gradient network approach is employed to adaptively adjust coupling strengths between oscillators.
  • The network structure evolves over time, with link configurations determined by preceding oscillator dynamics.
  • Incremental coupling adjustments are made based on the gradient network at each time step.

Main Results:

  • The proposed method successfully achieves synchronization in the Kuramoto model.
  • Numerical simulations confirm the effectiveness of the adaptive coupling strategy.
  • Synchronization is robustly achieved despite the presence of delayed couplings and external noise.

Conclusions:

  • The dynamical gradient network approach provides an effective mechanism for adaptive coupling in oscillator networks.
  • This method offers a promising strategy for controlling and achieving synchronization in complex systems.
  • The findings highlight the potential for robust synchronization in the Kuramoto model under realistic conditions.