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Related Experiment Video

Updated: Oct 29, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

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Modeling synchronization in globally coupled oscillatory systems using model order reduction.

Niccolò Discacciati1, Jan S Hesthaven1

  • 1Institute of Mathematics, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.

Chaos (Woodbury, N.Y.)
|July 9, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed reduced order models for synchronization in oscillatory systems like the Kuramoto model and circadian clocks. These models offer significant computational speedup for large-scale systems.

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Area of Science:

  • Complex systems
  • Nonlinear dynamics
  • Computational physics

Background:

  • Globally coupled multi-component oscillatory systems are fundamental in various scientific domains.
  • Synchronization phenomena in these systems are crucial for understanding collective behaviors.
  • Existing models can be computationally intensive for large-scale simulations.

Purpose of the Study:

  • To develop and validate reduced order models (ROMs) for globally coupled multi-component oscillatory systems.
  • To investigate the synchronization properties of the Kuramoto model and a circadian clock model using ROMs.
  • To assess the computational efficiency and accuracy of the proposed surrogate models.

Main Methods:

  • Construction of low-dimensional reduced order models for prototype oscillatory systems.
  • Analysis of synchronization transitions by varying coupling strengths in the reduced models.
  • Validation of reduced models against full system dynamics for accuracy and efficiency.

Main Results:

  • Reduced order models accurately approximate solutions and key quantities of interest.
  • The surrogate models successfully capture the transition to synchronized states.
  • Significant computational speedup is achieved for large systems when interactions depend on system variable averages.

Conclusions:

  • Reduced order modeling is an effective approach for studying synchronization in complex oscillatory systems.
  • The developed ROMs provide a computationally efficient alternative for large-scale simulations.
  • This methodology is applicable to diverse systems exhibiting collective dynamics and synchronization.