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Uncertainty quantification of multi-scale resilience in networked systems with nonlinear dynamics using arbitrary
Mengbang Zou1, Luca Zanotti Fragonara1, Song Qiu2
1School of Aerospace Transport and Manufacturing, Cranfield University, Cranfield, MK43 0AL, UK.
Scientific Reports
|January 10, 2023
Summary
This study quantifies model uncertainty's impact on complex system resilience. Using arbitrary polynomial chaos expansion, it identifies node-specific risks and parameter contributions for better interventions.
Area of Science:
- Complex Systems Science
- Network Science
- Resilience Engineering
Background:
- Complex systems exhibit sophisticated dynamics through interconnected components.
- System resilience, the ability to recover from perturbations, is crucial but challenging to analyze.
- Current understanding of networked resilience relies on simulations or graph topology, neglecting model uncertainty's impact on individual nodes.
Purpose of the Study:
- To quantify the effect of model uncertainty on complex system resilience across different network resolutions.
- To identify the probability of individual nodes losing resilience and the contribution of model parameters to this risk.
- To develop a framework for understanding macro-scale behavior and enabling micro-scale interventions.
Main Methods:
- Employed arbitrary polynomial chaos (aPC) expansion to analyze model uncertainty.
- Quantified resilience risk at both macro-scale network statistics and individual node dynamics.
- Tested the framework on a generic networked bi-stable system, ecological networks, and workforce commuter networks.
Main Results:
- The arbitrary polynomial chaos (aPC) expansion method successfully quantified uncertainty effects on resilience.
- Identified specific probabilities of node resilience loss and the influence of various model parameters.
- Demonstrated applicability across diverse network types, including ecological and commuter networks.
Conclusions:
- The developed framework provides a method to assess and understand model uncertainty's impact on complex system resilience.
- Enables practitioners to analyze macro-scale network behavior and implement targeted micro-scale interventions.
- Advances the field by integrating dynamics, topology, and uncertainty for a more comprehensive resilience analysis.
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