{\Pi}-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer

• URL: http://arxiv.org/abs/2304.12177v2
• Date: Thu, 10 Aug 2023 07:59:33 GMT
• Title: {\Pi}-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer
• Authors: Maximilian Pierzyna and Rudolf Saathof and Sukanta Basu
• Abstract summary: Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. We propose a physics-informed machine learning (ML) methodology, $Pi$-ML, based on dimensional analysis and gradient boosting to estimate $C_n2$.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 $R^2=0.958\pm0.001$.

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