Practical engineering problems often involve stochastic uncertainty, which can cause substantial variations in the response of engineering products or even lead to failure. The coupling and propagation of uncertainty play a crucial role in this process. Hence, it is imperative to quantify, propagate and control stochastic uncertainty. Different from most traditional uncertainty propagation methods, the proposed method employs Gaussian splitting method to divide the input random variables into Gaussian mixture models. These GMMs have a limited number of components with very small variances. As ...
