# Turbulence correction with artificial neural networks

arXiv: https://arxiv.org/abs/1806.07456 · HTML: https://mlsanity.com/paper/1806.07456/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Deep learning for enhanced free-space optical communications](https://mlsanity.com/paper/2208.07712/) (2022)
- [The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system](https://mlsanity.com/paper/2204.05227/) (2022)
- [Active Convolved Illumination with Deep Transfer Learning for Complex Beam Transmission through Atmospheric Turbulence](https://mlsanity.com/paper/2512.19540/) (2025)

## Abstract (arXiv)

We design an optical feedback network making use of machine learning techniques and demonstrate via simulations its ability to correct for the effects of turbulent propagation on optical modes. This artificial neural network scheme only relies on measuring the intensity profile of the distorted modes, making the approach simple and robust. The network results in the generation of various mode profiles at the transmitter that, after propagation through turbulence, closely resemble the desired target mode. The corrected optical mode profiles at the receiver are found to be nearly identical to the desired profiles, with near-zero mean square error indices. We are hopeful that the present results combining the fields of machine learning and optical communications will greatly enhance the robustness of free-space optical links.