Machine learning based optimization for interval uncertainty propagation

Two non-intrusive uncertainty propagation approaches are proposed for the performance analysis of engineering systems described by expensive-to-evaluate deterministic computer models with parameters defined as interval variables. These approaches employ a machine learning based optimization strategy, the so-called Bayesian optimization, for...

Reliability analysis of randomly excited FE modelled structures with interval mass and stiffness via sensitivity analysis

The present study focuses on reliability analysis of linear discretized structures with uncertain mass and stiffness parameters subjected to stationary Gaussian multi-correlated random excitation. Under the assumption that available information on the uncertain parameters is poor or incomplete, the interval model of uncertainty is adopted....