Towards a Data-Driven Estimation of Resilience in Networked Dynamical Systems: Designing a Versatile Testbed.
Tobias Fischer1,2, Thorsten Rings1,2, M Reza Rahimi Tabar3,4
1Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.
Frontiers in Network Physiology
|March 17, 2023
Summary
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.
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.
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