Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations

ArXi:2605.13790v1 Announce Type: cross Partial differential equations (PDEs) are fundamental for modeling complex natural and physical phenomena. In many real-world applications, however, observational data are extremely sparse, which severely limits the applicability of both classical numerical solvers and existing neural approaches. While neural methods have shown promising results under moderately sparse observations, their inference efficiency at high resolutions is limited, and their accuracy degrades substantially in the extremely sparse regime.