Physics-Informed Temporal U-Net for High-Fidelity Fluid Interpolation

ArXi:2604.23372v1 Announce Type: cross Reconstructing high-fidelity fluid dynamics from sparse temporal observations is quite challenging, mainly due to the chaotic and non-linear nature of fluid transport. Standard deep learning-based interpolation methods often tend to regress to the mean, which results in spatial blurring and temporal strobing, especially noticeable around the observed anchor frames where transitions become discontinuous.