Dear Neryvaldo

Model uncertainty cannot be taken into account without further information. Usually, it comes from comparing model predictions with experimental data / measurements: after calibrating your model at best (e.g. using Bayesian inversion techniques), there is some remaining *model/experiments mismatch* that you can consider as a model uncertainty.

Maybe what you mean is *surrogate model uncertainty* coming from the mismatch between the original finite element model and e.g. the Kriging model, that you would like to take it into account for further analysis. Is it what you refer to?

Well, for Kriging models, you have a local error coming from the Kriging variance. This is however (somehow) directly embedded in the AK-MCS procedure, that refines the surrogate until the surroagte error is negligible w.r.t the predicted quantities (such as the probability of failure).

For other surrogates, you can think about adding a centred Gaussian noise to the surrogate prediction, whose variance is linked to the LOO error of the surrogate. However, in common situations where this LOO error is in the range of 10^{-3} - 10^{-6} this is so negligible that people rarely do this in practice.

Hope it answers your questions.

Best regards

Bruno

Best regards

Bruno