Machine Learning

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 $C_n^2$....

We propose a data-driven threshold model to redefine the boundary between deposition and splashing for drop impact on dry smooth surfaces. The starting point is the collection and digitization of multiple experimental sources with varying impact conditions. The model is based on the theory of Riboux and Gordillo [Riboux and Gordillo, “Experiments of drops impacting a smooth solid surface: A model of the critical impact speed for drop splashing,” Phys. Rev....

Kelp forests are critical for marine ecosystems. They harbor a diverse range of species and maintain ecological balance, which necessitates the accurate monitoring of their evolution. We propose a multi-task ensemble deep learning framework to predict probabilistic maps of kelp forests from Landsat 7 satellite imagery. We train parallel image classification and segmentation models to achieve robust kelp predictions. Both model types are created as ensembles of 25 members producing probabilistic outputs....