Utilizing Machine Learning to Estimate Turbulence Strength Distribution Along a Path by Transmitting Single and bi-Direction Diverging Gaussian Beams
We use single and bi-direction diverging Gaussian beams to estimate turbulence strength distribution along a path utilizing machine learning. Our simulation results show a mean absolute error of 15% for single and 11% for bi-direction.
Authors
- Moshe Tur (ORCID: https://orcid.org/0000-0001-6799-8387)
- Ruoyu Zeng
- Yingning Wang (ORCID: https://orcid.org/0000-0001-7876-5722)
- Huibin Zhou (ORCID: https://orcid.org/0000-0001-5570-0950)
- Xinzhou Su (ORCID: https://orcid.org/0000-0003-2520-4475)
- Zile Jiang (ORCID: https://orcid.org/0009-0002-8114-4542)
- Yuxiang Duan (ORCID: https://orcid.org/0000-0002-7130-8251)
- Yue Zuo (ORCID: https://orcid.org/0009-0007-2104-3711)
- Zixun Zhao
- Heng Wu (ORCID: https://orcid.org/0009-0005-9609-894X)
- Alan E. Willner
- Hongkun Lian
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/cleo_si.2025.ss111_7
- Primary Topic
- Fluid Dynamics and Turbulent Flows
- Type
- article
- Field-Weighted Citation Impact
- 0.00