Deep learning neural network solution for predicting the refractive index structure constant

Shaun A. Comino

doi:10.1117/12.2631946

3 October 2022 Deep learning neural network solution for predicting the refractive index structure constant

Shaun A. Comino

Proceedings Volume 12227, Applications of Machine Learning 2022; 122270D (2022) https://doi.org/10.1117/12.2631946
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

A lightweight deep learning neural network model for predicting the refractive index structure constant (C²_n) was constructed using atmospheric data collected at the TISTEF laser range over a 90 day period. The model used variables such as barometric pressure, wind speed, wind direction, air temperature, corrected average ground temperature, relative humidity, solar kilowatts, solar mega joules, and various derived variables to make its predictions. The model was evaluated using measurements recorded from a Scintec BLS 900. The predictions from the model were highly correlated with the measured values of C²_n.

Conference Presentation

Citation Download Citation

Shaun A. Comino "Deep learning neural network solution for predicting the refractive index structure constant", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270D (3 October 2022); https://doi.org/10.1117/12.2631946

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