PINN-TurbNet for predicting atmospheric turbulence in LEO satellite-to-ground laser communication links

Atmospheric turbulence significantly impacts the performance of laser communication links between low Earth orbit (LEO) satellites and ground stations. In this study, we analyze minute-level meteorological data to reveal the spatial distribution of turbulence intensity. Based on these insights, we propose a physics-informed neural network model, PINN-TurbNet, which integrates physical constraints into a Transformer-based architecture to enhance turbulence prediction accuracy.