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Summary
This summary is machine-generated.

This study explores higher-order interactions in complex systems using coupled Rössler oscillators. Datasets reveal how linear and nonlinear coupling strengths influence synchronization in networks.

Keywords:
Complex networksElectronic circuitsHigher-order interactionsMaster stability functionNonlinear dynamics

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Area of Science:

  • Complex Systems
  • Nonlinear Dynamics
  • Network Science

Background:

  • Real-world systems often exhibit higher-order interactions beyond simple pairwise connections.
  • Modeling systems solely with pairwise interactions is insufficient for phenomena in the brain, social networks, and ecosystems.
  • The impact of higher-order interactions on collective behavior remains an open research question.

Purpose of the Study:

  • To experimentally investigate the consequences of higher-order interactions in complex systems.
  • To provide datasets on the dynamics of coupled nonlinear oscillators with both linear and nonlinear coupling.
  • To explore conditions for synchronization in networks with complex interaction structures.

Main Methods:

  • Utilized three nonlinear Rössler oscillators coupled in a simplicial complex.
  • Implemented four experimental scenarios varying linear (diffusive) and nonlinear (high-order) coupling through different variables (x, y, z).
  • Acquired 10,000 time series (30,000 points each) for each scenario, systematically modifying coupling strengths.

Main Results:

  • Generated experimental datasets detailing the dynamics of coupled Rössler oscillators under varied coupling conditions.
  • The data allows for corroboration of synchronization conditions based on linear and nonlinear coupling strengths.
  • The datasets can be used to test novel metrics for analyzing higher-order interactions.

Conclusions:

  • The study provides valuable experimental data for understanding synchronization in complex networks with higher-order interactions.
  • The findings contribute to the theoretical and numerical analysis of complex systems.
  • The datasets serve as a resource for future research on network dynamics and emergent behaviors.