  Uncertainty estimations are presented of the response of a multiscale
in-stent restenosis model, as obtained by both non-intrusive and semi-intrusive
uncertainty quantification. The in-stent restenosis model is a fully coupled
multiscale simulation of post-stenting tissue growth, in which the most costly
submodel is the blood flow simulation. Surrogate modeling for non-intrusive
uncertainty quantification takes the whole model as a black-box and maps
directly from the three uncertain inputs to the quantity of interest, the
neointimal area. The corresponding uncertain estimates matched the results from
quasi-Monte Carlo simulations well. In the semi-intrusive uncertainty
quantification, the most expensive submodel is replaced with a surrogate model.
We developed a surrogate model for the blood flow simulation by using a
convolutional neural network. The semi-intrusive method with the new surrogate
model offered efficient estimates of uncertainty and sensitivity while keeping
relatively high accuracy. It outperformed the result obtained with earlier
surrogate models. It also achieved the estimates comparable to the
non-intrusive method with similar efficiency. Presented results on uncertainty
propagation with non-intrusive and semi-intrusive metamodeling methods allow us
to draw some conclusions on the advantages and limitations of these methods.
