Deep Learning-based Surrogate Modelling of the LOD Method for Multiscale Problems

2026-07-14

AI Summary

This work addresses the challenge of efficiently and accurately solving multiscale elliptic partial differential equations with strongly heterogeneous or highly oscillatory coefficients, where conventional numerical methods suffer from high computational cost and existing neural operators lack sufficient accuracy. The authors propose LOD-MSNO, a hybrid model that uniquely integrates problem-adapted basis functions from the Local Orthogonal Decomposition (LOD) method as a strong prior within a neural operator framework. By leveraging data-driven learning, the approach alleviates the computational bottlenecks inherent in LOD while providing theoretical error estimates for coefficient learning. Numerical experiments demonstrate that, under strongly heterogeneous multiscale inputs, LOD-MSNO significantly outperforms current neural operator baselines, achieving markedly higher accuracy without compromising computational efficiency.

Abstract

Multiscale problems are notoriously difficult to tackle using traditional numerical methods, as accurately resolving fine-scale features often requires prohibitively fine discretizations. This challenge is particularly pronounced in applications such as materials science, fluid dynamics, climate systems, chemical processes, and complex networks. Recent neural operator models provide a promising data-driven alternative, but frequently struggle to achieve sufficient accuracy in the presence of strongly heterogeneous or oscillatory coefficients. In this work, we focus on the solution of elliptic PDEs with rough and high-contrast inputs. The Localized Orthogonal Decomposition (LOD) method is a well-established numerical approach for such problems, but it comes, however, at a substantial computational cost. We investigate the performance of popular neural operator architectures on these challenging multiscale problems and identify key limitations in their ability to resolve fine-scale structure. To overcome these challenges, we introduce LOD-MSNO (LOD-Multiscale Neural Operator), a hybrid approach that leverages the LOD method as a strong multiscale prior by building on its representation of the solution as a linear combination of problem-adapted basis functions, while addressing its main computational bottlenecks through data-driven operator learning. We further provide theoretical error estimates for the proposed coefficient-learning framework. Lastly, we demonstrate the potential of our proposed method to outperform current neural operator baselines in terms of accuracy for challenging multiscale inputs, while mainly retaining the computational efficiency of neural operator models.

Problem

• multiscale problems
• elliptic PDEs
• heterogeneous coefficients
• neural operators
• fine-scale features

Innovation

• LOD-MSNO
• neural operator
• multiscale modeling
• surrogate modeling
• elliptic PDEs