  Resilience is a system's ability to maintain its function when perturbations
and errors occur. Whilst we understand low-dimensional networked systems'
behavior well, our understanding of systems consisting of a large number of
components is limited. Recent research in predicting the network level
resilience pattern has advanced our understanding of the coupling relationship
between global network topology and local nonlinear component dynamics.
However, when there is uncertainty in the model parameters, our understanding
of how this translates to uncertainty in resilience is unclear for a
large-scale networked system. Here we develop a polynomial chaos expansion
method to estimate the resilience for a wide range of uncertainty
distributions. By applying this method to case studies, we not only reveal the
general resilience distribution with respect to the topology and dynamics
sub-models, but also identify critical aspects to inform better monitoring to
reduce uncertainty.
