When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains 事件

When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains arXiv:2605.08318v2 Announce Type: replace-cross Abstract: We study the problem of \emph{architecture selection} for deep learning models trained to solve partial differential equations (PDEs), asking when transformer-based architectures with learned attention outperform Fourier-domain neural operators. We introduce the \textbf{Multi-Scale Attention Transformer} (\msat{}), a deep learning architecture tha