Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

In the leave-one-out error formulation each sample of the experimental design is predicted using a model based on all other samples of the exper- imental design what is denoted M

Figure 1 shows the relative error in the second moment over the spatial domain of a stochastic Galerkin approximation to the solution of (4.7) using standard Hermite

We conclude that automatic selection, i.e., using cross-validation to choose a PCE solution from a number of candidate solutions computed with different methods on the same

Assuming that partial derivative evaluations of the computational model are available, we compute spectral expansions in terms of Poincaré basis functions or basis partial

Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful

An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis... An active-learning algorithm that combines

One of the sources for this error is the interpolation. One can reduce it by reducing the frequency step. In addition, close to the resonance frequency, the amplitude is driven

For the meta-models developed with EDs of size N = 500, Figure 26 depicts the evolution of err c C G with increasing response threshold; the left graph of the figure shows this