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, $\Pi$-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 $R^2=0.958\pm0.001$.

翻译：大气折射率的湍流扰动（即光学湍流）会显著扭曲激光束的传播。因此，建模这些扰动的强度（$C_n^2$）对未来自由空间光通信链路的成功开发与部署至关重要。本文提出一种基于物理信息的机器学习（ML）方法——Π-ML，该方法融合量纲分析与梯度提升技术来估算$C_n^2$。通过系统性特征重要性分析，我们识别出位温归一化方差是预测$C_n^2$的主导特征。为提升统计鲁棒性，我们训练了模型集成，其在样本外数据上表现优异，决定系数达到$R^2=0.958\pm0.001$。