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Semi-intrusive multiscale metamodelling uncertainty quantification with application to a model of in-stent restenosis
Author(s) -
Anikishova,
Lourens Veen,
Pavel Zun,
Alfons G. Hoekstra
Publication year - 2019
Publication title -
philosophical transactions of the royal society a mathematical physical and engineering sciences
Language(s) - English
Resource type - Journals
eISSN - 1471-2962
pISSN - 1364-503X
DOI - 10.1098/rsta.2018.0154
Subject(s) - metamodeling , restenosis , computer science , uncertainty quantification , black box , stent , monte carlo method , multiscale modeling , conformal map , artificial intelligence , mathematics , machine learning , medicine , software engineering , bioinformatics , surgery , mathematical analysis , statistics , biology
We explore the efficiency of a semi-intrusive uncertainty quantification (UQ) method for multiscale models as proposed by us in an earlier publication. We applied the multiscale metamodelling UQ method to a two-dimensional multiscale model for the wound healing response in a coronary artery after stenting (in-stent restenosis). The results obtained by the semi-intrusive method show a good match to those obtained by a black-box quasi-Monte Carlo method. Moreover, we significantly reduce the computational cost of the UQ. We conclude that the semi-intrusive metamodelling method is reliable and efficient, and can be applied to such complex models as the in-stent restenosis ISR2D model. This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.

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