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

Fuente: arXiv
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Hauptverfasser: Dai, Xiaoying, Hu, Miao, Shen, Shuwei
Format: Preprint
Veröffentlicht: 2026
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author Dai, Xiaoying
Hu, Miao
Shen, Shuwei
author_facet Dai, Xiaoying
Hu, Miao
Shen, Shuwei
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2601_10981
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A model order reduction based adaptive parareal method for time-dependent partial differential equations
Dai, Xiaoying
Hu, Miao
Shen, Shuwei
Numerical Analysis
Mathematical Physics
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.
title A model order reduction based adaptive parareal method for time-dependent partial differential equations
topic Numerical Analysis
Mathematical Physics
url https://arxiv.org/abs/2601.10981