A model order reduction based adaptive parareal method for time-dependent partial differential equations

TL;DR

This paper introduces an adaptive parareal method for time-dependent PDEs that integrates model order reduction to improve efficiency and accuracy in long-term simulations.

Contribution

It develops a novel adaptive parareal algorithm that constructs the coarse propagator using data-driven model order reduction techniques.

Findings

Effective in solving 3D advection-diffusion equations

Demonstrates good performance in long-term evolution simulations

Enhances parallel-in-time computational efficiency

Abstract

In this paper, we propose a model order reduction based adaptive parareal method for time-dependent partial differential equations. By using the data obtained by the fine propagator in each iteration of the plain parareal method together with some model order reduction technique, we construct the coarse propagator adaptively in each parareal iteration, and then obtain our adaptive parareal method. We apply this new method to solve some 3D time-dependent advection-diffusion equations with the Kolmogorov flow and the ABC flow. Numerical results show the good performance of our method in simulating long-term evolution problems.