Yingqi Hu

Fluid surrogate models have long reused U-shaped multiscale designs from vision, but their resampling, skip fusion and normalization were not built for flow fields. SUMNO spatially unrolls a multigrid V-cycle into a feed-forward U-shaped network, using parameter-free downsampling and bilinear upsampling for restriction and prolongation, and shifted-window attention at the coarsest level, with window masks set per-axis periodicity, for global coupling. Across seven settings on four flow benchmarks it is best in five and second in two, cutting 10-step rollout MSE by about 30% on Pinball and RBC, with 9.4M parameters.

Fluid surrogate models have long reused U-shaped multiscale designs from vision, but their resampling, skip fusion and normalization were not built for flow fields. SUMNO spatially unrolls a multigrid V-cycle into a feed-forward U-shaped network, using parameter-free downsampling and bilinear upsampling for restriction and prolongation, and shifted-window attention at the coarsest level, with window masks set per-axis periodicity, for global coupling. Across seven settings on four flow benchmarks it is best in five and second in two, cutting 10-step rollout MSE by about 30% on Pinball and RBC, with 9.4M parameters.