arXiv Machine Learning By Charles Arnal, Jacky H. T. Yip, Fran\c{c}ois Charton, Gary Shiu

Generating Special Triangulations with Transformers

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arXiv:2606. 26660v1 Announce Type: cross Abstract: Triangulations, i.

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arXiv Machine Learning
Jul 8

PGOT: A Physics-Geometry Operator Transformer for Complex PDEs

arXiv:2512. 23192v4 Announce Type: replace Abstract: While Transformers have demonstrated remarkable potential in modeling Partial Differential Equations (PDEs), modeling large-scale unstructured meshes with complex geometries remains a significant challenge.

By Zhuo Zhang, Xi Yang, Ying Miao, Xiaobin Hu, Yifu Gao, Yong Yang, Canqun Yang, Boocheong Khoo
arXiv Machine Learning
5d ago

Geometry-Aware Simplicial Message Passing

The paper introduces the Geometric Simplicial Weisfeiler–Lehman (GSWL) test, which extends the classic WL and SWL tests by incorporating vertex coordinates into color refinement for geometric simplicial complexes. It demonstrates that geometry‑aware simplicial message passing schemes are bounded by GSWL in expressivity and can match GSWL’s discriminating power on any fixed finite family of complexes. By combining GSWL with the Euler Characteristic Transform, the authors provide a complete invariant and an approximation framework, validated through experiments that reveal a clear hierarchy from combinatorial to geometry‑aware models.

By Elena Xinyi Wang, Bastian Rieck
arXiv Machine Learning
Jun 26

NervePool: A Simplicial Pooling Layer

arXiv:2305. 06315v3 Announce Type: replace-cross Abstract: For deep learning problems on graph-structured data, pooling layers are important for down sampling, reducing computational cost, and to minimize overfitting.

By Sarah McGuire Scullen, Ernst R\"oell, Elizabeth Munch, Bastian Rieck, Matthew Hirn
arXiv Machine Learning
Jun 3

CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization

arXiv:2605. 01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets.

By Ghadi Nehme, Eamon Whalen, Faez Ahmed