Papers › Towards Universal Mesh Movement Networks

Towards Universal Mesh Movement Networks

29 Jun 2024arXiv:2407.00382archive 2025-07-28

Mingrui Zhang, Chunyang Wang, Stephan Kramer, Joseph G. Wallwork, Siyi Li, Jiancheng Liu, Xiang Chen, Matthew D. Piggott

Solving complex Partial Differential Equations (PDEs) accurately and efficiently is an essential and challenging problem in all scientific and engineering disciplines. Mesh movement methods provide the capability to improve the accuracy of the numerical solution without increasing the overall mesh degree of freedom count. Conventional sophisticated mesh movement methods are extremely expensive and struggle to handle scenarios with complex boundary geometries. However, existing learning-based methods require re-training from scratch given a different PDE type or boundary geometry, which limits their applicability, and also often suffer from robustness issues in the form of inverted elements. In this paper, we introduce the Universal Mesh Movement Network (UM2N), which -- once trained -- can be applied in a non-intrusive, zero-shot manner to move meshes with different size distributions and structures, for solvers applicable to different PDE types and boundary geometries. UM2N consists of a Graph Transformer (GT) encoder for extracting features and a Graph Attention Network (GAT) based decoder for moving the mesh. We evaluate our method on advection and Navier-Stokes based examples, as well as a real-world tsunami simulation case. Our method outperforms existing learning-based mesh movement methods in terms of the benchmarks described above. In comparison to the conventional sophisticated Monge-Amp\`ere PDE-solver based method, our approach not only significantly accelerates mesh movement, but also proves effective in scenarios where the conventional method fails. Our project page is at https://erizmr.github.io/UM2N/.

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GlobalFeatExtractor mesh-adaptation/um2n/UM2N/model/M2N.py official repository ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · 52b419a6d667246e · report
get_sample_param_of_nu_generalization_by_idx_train mesh-adaptation/um2n/script/evaluate.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 76d4374c717e46d6 · report
weighted_mse mesh-adaptation/um2n/script/train_model.py official repository ran · our draft was wrong MIT (permissive) · 39c97f78043b08e8 · report
DeformGAT mesh-adaptation/um2n/UM2N/model/M2N.py official repository unverified MIT (permissive) · 0d971f4cea200e5e · report
LocalFeatExtractor mesh-adaptation/um2n/UM2N/model/M2N.py official repository unverified MIT (permissive) · 41f037911d073d0a · report
M2N mesh-adaptation/um2n/UM2N/model/M2N.py official repository unverified MIT (permissive) · 188f779b4abc2944 · report
NetGATDeform mesh-adaptation/um2n/UM2N/model/M2N.py official repository unverified MIT (permissive) · 7264f9a78256f027 · report

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