A Physics-Guided Machine Learning Model for the Prediction of Atmospheric Refraction

Wardeh Al-Younis; Steven Sandoval; David Voelz; Mohammad Abdullah-Al-Mamun

doi:10.1364/PCAOP.2022.PTh4F.4

Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)
Technical Digest Series (Optica Publishing Group, 2022),
paper PTh4F.4
•https://doi.org/10.1364/PCAOP.2022.PTh4F.4

A Physics-Guided Machine Learning Model for the Prediction of Atmospheric Refraction

Wardeh Al-Younis, Steven Sandoval, David Voelz, and Mohammad Abdullah-Al-Mamun

Propagation Through and Characterization of Atmospheric and Oceanic Phenomena 2022

Vancouver, British Columbia Canada
11–15 July 2022
ISBN: 978-1-957171-09-8

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AI and Machine Learning in pcAOP (PTh4F)

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W. Al-Younis, S. Sandoval, D. Voelz, and M. Abdullah-Al-Mamun, "A Physics-Guided Machine Learning Model for the Prediction of Atmospheric Refraction," in Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP), Technical Digest Series (Optica Publishing Group, 2022), paper PTh4F.4.

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Abstract

Image feature shifts due to atmospheric refraction are predicted with a finite difference machine learning method based on physics-infused modeling and data-driven training using time-lapse imagery. The model’s performance is compared with a previous approach.

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