Stabilizing PDE--ML coupled systems

Fuente: arXiv
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Main Authors: Qadeer, Saad, Stinis, Panos, Wan, Hui.
Format: Preprint
Published: 2025
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author Qadeer, Saad
Stinis, Panos
Wan, Hui.
author_facet Qadeer, Saad
Stinis, Panos
Wan, Hui.
contents A long-standing obstacle in the use of machine-learnt surrogates with larger PDE systems is the onset of instabilities when solved numerically. Efforts towards ameliorating these have mostly concentrated on improving the accuracy of the surrogates or imbuing them with additional structure, and have garnered limited success. In this article, we study a prototype problem and draw insights that can help with more complex systems. In particular, we focus on a viscous Burgers'-ML system and, after identifying the cause of the instabilities, prescribe strategies to stabilize the coupled system. To improve the accuracy of the stabilized system, we next explore methods based on the Mori--Zwanzig formalism.
format Preprint
id arxiv_https___arxiv_org_abs_2506_19274
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Stabilizing PDE--ML coupled systems
Qadeer, Saad
Stinis, Panos
Wan, Hui.
Numerical Analysis
Machine Learning
65M12
A long-standing obstacle in the use of machine-learnt surrogates with larger PDE systems is the onset of instabilities when solved numerically. Efforts towards ameliorating these have mostly concentrated on improving the accuracy of the surrogates or imbuing them with additional structure, and have garnered limited success. In this article, we study a prototype problem and draw insights that can help with more complex systems. In particular, we focus on a viscous Burgers'-ML system and, after identifying the cause of the instabilities, prescribe strategies to stabilize the coupled system. To improve the accuracy of the stabilized system, we next explore methods based on the Mori--Zwanzig formalism.
title Stabilizing PDE--ML coupled systems
topic Numerical Analysis
Machine Learning
65M12
url https://arxiv.org/abs/2506.19274