Non-intrusive and semi-intrusive uncertainty quantification of a multiscale in-stent restenosis model

Dongwei Ye*, Anna Nikishova, Lourens Veen, Pavel Zun, Alfons G. Hoekstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
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The In-Stent Restenosis 2D model is a full y coupled multiscale simulation of post-stenting tissue growth, in which the most costly submodel is the blood flow simulation. This paper presents uncertainty estimations of the response of this model, as obtained by both non-intrusive and semi-intrusive uncertainty quantification. A surrogate model based on Gaussian process regression for non-intrusive uncertainty quantification takes the whole model as a black-box and maps directly 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 a relatively high accuracy. It outperformed the results obtained with earlier surrogate models. It also achieved the estimates comparable to the non-intrusive method with a similar efficiency. Presented results on uncertainty propagation with non-intrusive and semi-intrusive metamodelling methods allow us to draw some conclusions on the advantages and limitations of these methods.

Original languageEnglish
Article number107734
JournalReliability Engineering and System Safety
Publication statusPublished - 1 Oct 2021

Bibliographical note

Funding Information:
This work was supported by the Netherlands eScience Center under grant agreement 27015G01 (e-MUSC project). This project has received funding from the European Union Horizon 2020 research and innovation programme, The Netherlands under grant agreements #800925 (VECMA project) and #777119 (InSilc project). PZ has received funding from The Russian Foundation for Basic Research under agreement #18-015-00504 and from the Russian Science Foundation under agreement #20-71-10108 . This work was sponsored by NWO Exacte Wetenschappen (Physical Sciences), The Netherlands for the use of supercomputer facilities, with financial support from the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organization for Science Research, NWO) .

Publisher Copyright:
© 2021 Elsevier B.V.


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