Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting

ArXi:2601.21151v2 Announce Type: replace Recent machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms-advection (long-range transport), diffusion-like mixing, thermodynamic processes, and forcing-are represented implicitly within a single large network. This is particularly problematic for advection, where long-range transport typically requires expensive global interaction mechanisms or deep stacks of local convolutional layers.