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

Atmospheric turbulence significantly affects the quality of laser communication links between low Earth orbit (LEO) satellites and ground stations. To enable accurate prediction of turbulence around ground stations, this study conducts a quantitative analysis of the communication link and presents the spatial distribution of atmospheric turbulence intensity. We propose a physics-informed neural network model, PINN-TurbNet, which integrates high-resolution meteorological parameters with physical constraints to enhance prediction accuracy. Furthermore, a multi-layer atmospheric turbulence analysis approach is introduced to improve computational efficiency. Experimental results demonstrate that the PINN-TurbNet model achieves a relative mean squared error (MSE) of 0.318 and a relative mean absolute error (MAE) of 0.209.