Abstract The crude Monte Carlo method is computationally expensive. Hence, incorporating model order reduction methods enabling reliability analysis for high‐dimensional problems necessary. However, this strategy may result in an inaccurate estimation of the probability failure rare events two reasons. First, reduction, represented by proper orthogonal decomposition (POD) here, requires respons...

Reduced-order models are essential tools to deal with parametric problems in the context of optimization, uncertainty quantification, or control and inverse problems. The set solutions lies a low-dimensional manifold (with dimension equal number independent parameters) embedded large-dimensional space (dimension degrees freedom full-order discrete model). A posteriori model reduction is based o...