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papersTODAY 04:00 UTC

LiftGCN applies Joukowski spectral lifting to finite element stress prediction

Researchers introduce LiftGCN, a graph learning method designed to predict finite element stress fields that contain sharp gradients near holes, notches and load points. The approach uses a Joukowski spectral lifting transform to preserve energy and retain high-frequency graph components that standard graph neural networks tend to smooth away. The work is posted as an arXiv preprint in computer science categories.

arXiv LiftGCN Joukowski spectral lifting finite element stress prediction graph neural networks

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arXiv cs.AILiftGCN: Efficient Energy-Preserving Graph Learning via Joukowski Spectral Lifting for Finite Element Stress Prediction ↗TODAY 04:00 UTC

arXiv cs.LGLiftGCN: Efficient Energy-Preserving Graph Learning via Joukowski Spectral Lifting for Finite Element Stress Prediction ↗TODAY 04:00 UTC