AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915539361726464 |
|---|---|
| author | Freymuth, Niklas Würth, Tobias Schreiber, Nicolas Gyenes, Balazs Boltres, Andreas Mitsch, Johannes Taranovic, Aleksandar Hoang, Tai Dahlinger, Philipp Becker, Philipp Kärger, Luise Neumann, Gerhard |
| author_facet | Freymuth, Niklas Würth, Tobias Schreiber, Nicolas Gyenes, Balazs Boltres, Andreas Mitsch, Johannes Taranovic, Aleksandar Hoang, Tai Dahlinger, Philipp Becker, Philipp Kärger, Luise Neumann, Gerhard |
| contents | The cost and accuracy of simulating complex physical systems using the Finite Element Method (FEM) scales with the resolution of the underlying mesh. Adaptive meshes improve computational efficiency by refining resolution in critical regions, but typically require task-specific heuristics or cumbersome manual design by a human expert. We propose Adaptive Meshing By Expert Reconstruction (AMBER), a supervised learning approach to mesh adaptation. Starting from a coarse mesh, AMBER iteratively predicts the sizing field, i.e., a function mapping from the geometry to the local element size of the target mesh, and uses this prediction to produce a new intermediate mesh using an out-of-the-box mesh generator. This process is enabled through a hierarchical graph neural network, and relies on data augmentation by automatically projecting expert labels onto AMBER-generated data during training. We evaluate AMBER on 2D and 3D datasets, including classical physics problems, mechanical components, and real-world industrial designs with human expert meshes. AMBER generalizes to unseen geometries and consistently outperforms multiple recent baselines, including ones using Graph and Convolutional Neural Networks, and Reinforcement Learning-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction Freymuth, Niklas Würth, Tobias Schreiber, Nicolas Gyenes, Balazs Boltres, Andreas Mitsch, Johannes Taranovic, Aleksandar Hoang, Tai Dahlinger, Philipp Becker, Philipp Kärger, Luise Neumann, Gerhard Machine Learning Computational Geometry The cost and accuracy of simulating complex physical systems using the Finite Element Method (FEM) scales with the resolution of the underlying mesh. Adaptive meshes improve computational efficiency by refining resolution in critical regions, but typically require task-specific heuristics or cumbersome manual design by a human expert. We propose Adaptive Meshing By Expert Reconstruction (AMBER), a supervised learning approach to mesh adaptation. Starting from a coarse mesh, AMBER iteratively predicts the sizing field, i.e., a function mapping from the geometry to the local element size of the target mesh, and uses this prediction to produce a new intermediate mesh using an out-of-the-box mesh generator. This process is enabled through a hierarchical graph neural network, and relies on data augmentation by automatically projecting expert labels onto AMBER-generated data during training. We evaluate AMBER on 2D and 3D datasets, including classical physics problems, mechanical components, and real-world industrial designs with human expert meshes. AMBER generalizes to unseen geometries and consistently outperforms multiple recent baselines, including ones using Graph and Convolutional Neural Networks, and Reinforcement Learning-based approaches. |
| title | AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction |
| topic | Machine Learning Computational Geometry |
| url | https://arxiv.org/abs/2505.23663 |