LiftGCN: Efficient Energy-Preserving Graph Learning via Joukowski Spectral Lifting for Finite Element Stress Prediction

arXiv:2609.14977 · cs.LG, cs.AI · Submitted 2026-09-14 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-14

Updated: 2026-09-14

Code: https://github.com/ChenZeng001/LiftGCN

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 18× in this paper · explained in 1 episode
• Aggregation — 11× in this paper · explained in 1 episode
• alpha — 6× in this paper · explained in 1 episode
• Ablation — 5× in this paper · explained in 1 episode
• Ablation Studies — 3× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Graph Construction — 2× in this paper · explained in 3 episodes
• Over-smoothing — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes

Sources

• Semi-Supervised Classification with Graph Convolutional Networks

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