Π-ML

Tailor-made data-driven similarity theories for the temperature structure parameter $C_T^2$ in the lower atmospheric boundary layer

Optical ground-based astronomy and future free-space optical communication (FSOC) suffer from light getting distorted when it propagates through the turbulent atmosphere. In astronomy, turbulent fluctuations of the atmospheric refractive index, known as optical turbulence (OT), cause blurry images and limit the detection of small objects. FSOC links, which use optical beams to transmit data instead of traditional radio waves, experience reduced data rates or even link interruptions due to laser beams getting distorted by OT. ...

Π-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer

Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations ($C_n^2$) is highly relevant for the successful development and deployment of future free-space optical communication links. In this letter, we propose a physics-informed machine learning (ML) methodology, Π-ML, based on dimensional analysis and gradient boosting to estimate $C_n^2$. Through a systematic feature importance analysis, we identify the normalized variance of potential temperature as the dominating feature for predicting $C_n^2$. For statistical robustness, we train an ensemble of models which yields high performance on the out-of-sample data of $R^2=0.958\pm0.001$. ...