AINeutralarXiv – CS AI · 10h ago6/10
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When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains
Researchers introduce Multi-Scale Attention Transformer (MSAT), a deep learning architecture that outperforms Fourier-based neural operators for solving PDEs on irregular domains. The model achieves 3.7x better accuracy than FNO on complex geometry problems while running 3,500x faster than competing approaches, with theoretical bounds explaining when attention mechanisms beat frequency-domain methods.