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

  title={Non-intrusive and semi-intrusive uncertainty quantification of a multiscale in-stent restenosis model},
  author={Dongwei Ye and Anna Nikishova and Lourens E. Veen and Pavel S. Zun and Alfons G. Hoekstra},
  journal={Reliab. Eng. Syst. Saf.},

Figures and Tables from this paper

Uncertainty quantification patterns for multiscale models
This work presents uncertainty quantification patterns (UQPs) that are designed to support the analysis of uncertainty in coupled multi-scale and multi-domain applications and presents the implementation of the UQPs with multiscale coupling toolkit Multiscale Coupling Library and Environment 3.0.
Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models
The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective.
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
Temperature field reconstruction is essential for analyzing satellite heat reliability. As a representative machine learning model, the deep convolutional neural network (DCNN) is a powerful tool for
Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling
In-stent restenosis is a recurrence of coronary artery narrowing due to vascular injury caused by balloon dilation and stent placement. It may lead to the relapse of angina symptoms or to an acute
Uncertainty quantification of a 3D In-Stent Restenosis model with surrogate modelling
A surrogate model, based on Gaussian process regression with proper orthogonal decomposition, was developed which subsequently replaced the original In-Stent Restenosis model in the uncertainty quantification.
VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations
A range of functional and performance improvements that have been introduced, newly introduced components, and applications examples from seven different domains such as conflict modelling and environmental sciences are covered.


Semi-intrusive multiscale metamodelling uncertainty quantification with application to a model of in-stent restenosis
It is concluded 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.
Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis
The quasi-Monte Carlo UQ and the Sobol sensitivity analysis are reliable methods for estimating uncertainties in the response of complicated multiscale cardiovascular models.
Semi-intrusive uncertainty propagation for multiscale models
Location-Specific Comparison Between a 3D In-Stent Restenosis Model and Micro-CT and Histology Data from Porcine In Vivo Experiments
An approach for validation of an in silico 3D model of in-stent restenosis in porcine coronary arteries is reported on and good agreement was obtained for both the overall amount of neointima produced and the local distribution.
A Comparison of Fully-Coupled 3D In-Stent Restenosis Simulations to In-vivo Data
A fully-coupled 3D multiscale model of in-stent restenosis, with blood flow simulations coupled to smooth muscle cell proliferation, and results of numerical simulations performed are described and reported.
Multi-scale simulations of the dynamics of in-stent restenosis: impact of stent deployment and design
Simulation results suggest that the growth of the restenotic lesion is strongly dependent on the stent strut cross-sectional profile, and a strong correlation between strut thickness and the rate of smooth muscle cell proliferation has been observed.
A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis
This study marks, to the authors' knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.
Modelling the Effect of a Functional Endothelium on the Development of In-Stent Restenosis
The data indicate a positive correlation between the neointimal growths and strut deployment depths in the presence of a functional endothelium, in qualitative agreement with in-vivo data.
The application of multiscale modelling to the process of development and prevention of stenosis in a stented coronary artery
  • D. Evans, P. Lawford, A. Hoekstra
  • Computer Science
    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
  • 2008
This paper focuses on methodology, introduces the concept of the CxA and demonstrates its use in the generation of a multiscale model of the physical and biological processes implicated in a challenging and clinically relevant problem, namely coronary artery in-stent restenosis.