The Deep Wind Method: Physics-Informed Wind Field Reconstruction with Mass Consistency

We present the Deep Wind methodology, a physics-informed neural network (PINN) formulation for reconstructing three-dimensional wind fields from incomplete and noisy data. The approach embeds mass conservation and boundary conditions directly into the loss function, enabling physically consistent and stable reconstructions without mesh-based discretization. A series of synthetic benchmarks and real observations from Super Typhoon Kong-Rey (2024) demonstrate the robustness of the method compared to classical variational approaches. We show that Deep Wind consistently maintains stability and accuracy under sparse, irregular, or noisy observations. Overall, the results suggest that physics-informed deep learning is a promising framework for wind field recovery and data assimilation, particularly in meteorology and wind energy.