Model reduction methods usually focus on the error performance analysis; however, in
presence of uncertainties, it is important to analyze the robustness properties of the error in
model reduction as well. In this paper, we give robustness guarantees for structured model
reduction of linear and nonlinear dynamical systems under parametric uncertainties. In
particular, we consider a model reduction where the states in the reduced model are a strict
subset of the states of the full model, and the dynamics for all other states are collapsed to …

Abstract Model reduction methods usually focus on the error performance analysis;
however, in presence of uncertainties, it is important to analyze the robustness properties of
the error in model reduction as well. This problem is particularly relevant for engineered
biological systems that need to function in a largely unknown and uncertain environment.
We give robustness guarantees for structured model reduction of linear and nonlinear
dynamical systems under parametric uncertainties. We consider a model reduction problem …