Efficient coarse correction for parallel time-stepping in plaque growth simulations

TL;DR

This paper introduces an interpolation-based coarse propagator for parallel time-stepping in plaque growth simulations, significantly reducing computational costs and improving scalability in simulating atherosclerotic plaque development.

Contribution

It presents a novel interpolation-based coarse propagator that leverages micro-scale growth data to enhance parallel time integration efficiency in plaque growth modeling.

Findings

Reduces computational work per parareal iteration

Decreases the number of iterations needed for convergence

Improves scalability of plaque growth simulations

Abstract

In order to make the numerical simulation of atherosclerotic plaque growth feasible, a temporal homogenization approach is employed. The resulting macro-scale problem for the plaque growth can be further accelerated by using parallel time integration schemes, such as the parareal algorithm. However, the parallel scalability is dominated by the computational cost of the coarse propagator. Therefore, in this paper, an interpolation-based coarse propagator, which uses growth values from previously computed micro-scale problems, is introduced. For a simple model problem, it is shown that this approach reduces both the computational work for a single parareal iteration as well as the required number of parareal iterations.