ML-based approach to classification and generation of structured light propagation in turbulent media
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915939305390080 |
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| author | Wang, Aokun Nair, Anjali Wang, Zhongjian Bal, Guillaume |
| author_facet | Wang, Aokun Nair, Anjali Wang, Zhongjian Bal, Guillaume |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14208 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ML-based approach to classification and generation of structured light propagation in turbulent media Wang, Aokun Nair, Anjali Wang, Zhongjian Bal, Guillaume Optics Machine Learning Optimization and Control Computational Physics 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. |
| title | ML-based approach to classification and generation of structured light propagation in turbulent media |
| topic | Optics Machine Learning Optimization and Control Computational Physics |
| url | https://arxiv.org/abs/2604.14208 |