Uncertainties Propagation through Robust Reduced Model

Designing large-scale systems in which parametric uncertainties and localized nonlinearities are incorporated requires the implementation of both uncertainty propagation and robust model condensation methods. In this context, we propose to propagate uncertainties through a model, which combines the statistical Latin Hypercube Sampling (LHS) technique and a robust condensation method. The latter is based on the enrichment of a truncated eigenvectors bases using static residuals taking into account parametric uncertainty and localized nonlinearity effects. The efficiency, in terms of accuracy and time consuming, of the proposed method is evaluated on the nonlinear time response of a 2D frame structure.