AI RESEARCH
DRIFT-Net: A Spectral--Coupled Neural Operator for PDEs Learning
arXiv CS.LG
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ArXi:2509.24868v3 Announce Type: replace Learning PDE dynamics with neural solvers can significantly improve wall-clock efficiency and accuracy compared with classical numerical solvers. In recent years, foundation models for PDEs have largely adopted multi-scale windowed self-attention, with the scOT backbone in Poseidon serving as a representative example. However, because of their locality, truly globally consistent spectral coupling can only be propagated gradually through deep stacking and window shifting.