multi-scale

HI-MGN introduces a hierarchical multiscale graph neural network to improve long-range communication in mesh-based physics simulations, enhancing accuracy while reducing training time and memory usage compared to existing methods.

CAMP introduces a cycle-aware multi-scale patch mixer for time series forecasting, achieving state-of-the-art results on multiple benchmarks through adaptive cycle learning and horizon-guided patch refinement.

This paper proposes Physics-Informed Multi-Scale Mamba (PIMSM), a state-space architecture that aligns model memory with physical timescales to improve robustness under distribution shift in scientific time series, demonstrating improvements on fMRI and weather forecasting tasks.

This paper proposes M2SNet, a multi-scale multi-scale subtraction network for medical image segmentation that uses subtraction operations to capture difference features between encoder levels, improving localization and edge sharpness. It achieves state-of-the-art performance on eleven datasets across four medical imaging modalities.