Multiphysics embedding localized orthogonal decomposition for thermomechanical coupling problems
Yuzhou Nan, Yajun Wang, Changqing Ye, Xiaofei Guan

TL;DR
This paper introduces a novel multiphysics embedding localized orthogonal decomposition (ME-LOD) method for efficiently solving complex thermomechanical coupling problems in heterogeneous media, improving accuracy and stability over traditional methods.
Contribution
The paper presents a unified multiscale method that constructs a single multiscale space for both physical fields, enhancing stability and efficiency in multiphysics modeling.
Findings
ME-LOD outperforms standard LOD in accuracy for high-contrast materials
The method maintains computational efficiency while improving stability
Numerical experiments validate the effectiveness of the proposed approach
Abstract
Multiscale modeling and analysis of multiphysics coupling processes in highly heterogeneous media present significant challenges. In this paper, we propose a novel multiphysics embedding localized orthogonal decomposition (ME-LOD) method for solving thermomechanical coupling problems, which also provides a systematic approach to address intricate coupling effects in multiphysical systems. Unlike the standard localized orthogonal decomposition (LOD) method that constructs separate multiscale spaces for each physical field, the proposed method features a unified construction for both displacement and temperature. Compared to the standard LOD method, our approach achieves operator stability reconstruction through orthogonalization while preserving computational efficiency. Several numerical experiments demonstrate that the ME-LOD method outperforms the traditional LOD method in accuracy,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsModel Reduction and Neural Networks · Matrix Theory and Algorithms · Advanced Mathematical Modeling in Engineering
