Research

Figure: Comparision of $C_n^2$ (markers) observed at two levels with $C_n^2$ time series (line) estimated from mesoscale simulations using a variance-based parameterization.

For free-space optical communication (FSOC) or ground-based optical astronomy, abundant data of optical turbulence strength ($C_n^2$) is imperative but typically scarce. Turbulence conditions are strongly site-dependent, so their accurate quantification requires in-situ measurements or numerical weather simulations. If $C_n^2$ is not measured directly, e.g., with a scintillometer, $C_n^2$ parameterizations must be utilized to estimate $C_n^2$ from meteorological observations or model output. Even though various such parameterizations exist in the literature, their relative performance is unknown....

Figure: kelpnet_header.jpg

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....

Figure: Ensemble $C_n^2$ predictions from Weather Research and Forecasting model compared to observed $C_n^2$ from two scintillometers.

Free-space optical communication (FSOC) links are considered a key technology to support the increasing needs of our connected, data-heavy world, but they are prone to disturbance through atmospheric processes such as optical turbulence. Since turbulence is highly dependent on local topographic and meteorological conditions, modeling optical turbulence strength $C_n^2$ is challenging during the design phase of an optical link or network. Over the past 25 years, $C_n^2$ parameterizations of varying complexities have been combined with various numerical weather prediction models for the spatio-temporal estimation of $C_n^2$....

Figure: The two components of our $\Pi$-ML approach: Combination of dimensional variables into non-dimensional $\Pi$-variables and the subsequent XGBoost ensemble training.

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 $C_n^2$....

Figure: Schematic of our three-step data-driven approach.

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....