Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs

ArXi:2410.02113v3 Announce Type: replace Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have emerged as the preferred architecture for PDEs due to their ability to capture intricate dependencies. However, they struggle with representing continuous dynamics and long-range interactions. To overcome these limitations, we