Guimok Cho — accepted AI-conference papers

2026

EqGINO: Equivariant Geometry-Informed Fourier Neural Operators for 3D Partial Differential Equations

Sungwon Kim, Juho Song, Seungmin Shin, Guimok Cho, Sangkook Kim, Chanyoung Park

ICML 2026poster

Deep learning surrogates for 3D Partial Differential Equations (PDEs) often fail to generalize across geometric transformations because they depend heavily on specific coordinate systems. While equivariant networks offer a solution, they typically rely on local operations in the spatial domain, maki…

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2025

Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks

Sungwon Kim, Namkyeong Lee, Yunyoung Doh, Seungmin Shin, Guimok Cho, Seung-Won Jeon +2

ICML 2025poster

Mesh-based 3D static analysis methods have recently emerged as efficient alternatives to traditional computational numerical solvers, significantly reducing computational costs and runtime for various physics-based analyses. However, these methods primarily focus on surface topology and geometry, of…

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