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Towards a Data-Driven Estimation of Resilience in Networked Dynamical Systems: Designing a Versatile Testbed.

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Estimating system resilience is difficult, especially with data-driven methods. This study introduces a testbed to modify and measure resilience in networked systems, aiding the development of better estimation techniques.

Keywords:
FitzHugh-Nagumocomplex networkscoupled oscillatorsresiliencetestbedtime series analysis

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Area of Science:

  • Complex Systems Science
  • Network Dynamics
  • Systems Resilience

Background:

  • Estimating the resilience of adaptive, networked dynamical systems is a significant challenge in complex systems science.
  • Current methods often require detailed knowledge of system dynamics or lack robust validation for data-driven approaches.
  • Resilience is defined as a system's capacity to absorb disturbances and reorganize while maintaining core functions and structure.

Purpose of the Study:

  • To develop a controlled testbed for modifying and evaluating the resilience of multistable networked dynamical systems.
  • To generate multivariate time series data for assessing data-driven resilience estimation techniques.
  • To address the limitations of existing methods for quantifying system resilience from observational data.

Main Methods:

  • Development of a novel testbed for controlled manipulation of system resilience.
  • Generation of multivariate time series data from the engineered dynamical system.
  • Utilizing the testbed to evaluate the performance and suitability of data-driven resilience estimators.

Main Results:

  • Successfully created a testbed capable of systematically altering the resilience of a multistable networked system.
  • Generated comprehensive time series data suitable for rigorous testing of resilience estimation algorithms.
  • Reported initial findings on the performance of a specific data-driven resilience estimator within the testbed environment.

Conclusions:

  • The developed testbed provides a valuable tool for advancing the field of data-driven resilience estimation.
  • The findings offer insights into the capabilities and limitations of current data-driven approaches for assessing system resilience.
  • This work facilitates more reliable quantification of resilience in complex adaptive systems.