Abstract:This work develops machine learning approaches to classify structured light wave beams developing random speckle disturbances as they propagate through turbulent atmospheres. Beam propagation is modeled by the numerical simulation of a stochastic paraxial equation. We design convolutional neural networks tailored for this specific application and use them for a classification model with one-hot encoding. To address the challenge of potentially limited available data, we develop a prediction-based generative diffusion model to provide additional data during classifier training. We show that a Bregman distance minimization during the learning step improves the quality of the generation of high-frequency modes.
| Subjects: | Optics (physics.optics); Machine Learning (cs.LG); Optimization and Control (math.OC); Computational Physics (physics.comp-ph) |
| Cite as: | arXiv:2604.14208 [physics.optics] |
| (or arXiv:2604.14208v1 [physics.optics] for this version) | |
| https://doi.org/10.48550/arXiv.2604.14208 arXiv-issued DOI via DataCite (pending registration) |
From: Anjali Nair [view email]
[v1]
Sat, 4 Apr 2026 16:58:53 UTC (17,911 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。