Generation of Higher-Order Hermite–Gaussian Modes Based on Physical Model and Deep Learning
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| Udgivet i: | Photonics vol. 12, no. 8 (2025), p. 801-813 |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2304-6732 | ||
| 024 | 7 | |a 10.3390/photonics12080801 |2 doi | |
| 035 | |a 3244049400 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231546 |2 nlm | ||
| 100 | 1 | |a Chen, Tai |u School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing 404100, China; taichen04@163.com (T.C.); chengcaijiang2003@163.com (C.J.); taojia2005@163.com (J.T.) | |
| 245 | 1 | |a Generation of Higher-Order Hermite–Gaussian Modes Based on Physical Model and Deep Learning | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The higher-order Hermite–Gaussian (HG) modes exhibit complex spatial distributions and find a wide range of applications in fields such as quantum information processing, optical communications, and precision measurements. In recent years, the advancement of deep learning has emerged as an effective approach for generating higher-order HG modes. However, the traditional data-driven deep learning method necessitates a substantial amount of labeled data for training, entails a lengthy data acquisition process, and imposes stringent requirements on system stability. In practical applications, these methods are confronted with challenges such as the high cost of data labeling. This paper proposes a method that integrates a physical model with deep learning. By utilizing only a single intensity distribution of the target optical field and incorporating the physical model, the training of the neural network can be accomplished, thereby eliminating the dependency of traditional data-driven deep learning methods on large datasets. Experimental results demonstrate that, compared with the traditional data-driven deep learning method, the method proposed in this paper yields a smaller root mean squared error between the generated higher-order HG modes. The quality of the generated modes is higher, while the training time of the neural network is shorter, indicating greater efficiency. By incorporating the physical model into deep learning, this approach overcomes the limitations of traditional deep learning methods, offering a novel solution for applying deep learning in light field manipulation, quantum physics, and other related fields. | |
| 653 | |a Data acquisition | ||
| 653 | |a Spatial distribution | ||
| 653 | |a Quantum phenomena | ||
| 653 | |a Data processing | ||
| 653 | |a Deep learning | ||
| 653 | |a Hermite-Gaussian modes | ||
| 653 | |a Neural networks | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Communication | ||
| 653 | |a Electron microscopes | ||
| 653 | |a Optical data processing | ||
| 653 | |a Information processing | ||
| 653 | |a Algorithms | ||
| 653 | |a Quantum theory | ||
| 653 | |a Systems stability | ||
| 700 | 1 | |a Jiang Chengcai |u School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing 404100, China; taichen04@163.com (T.C.); chengcaijiang2003@163.com (C.J.); taojia2005@163.com (J.T.) | |
| 700 | 1 | |a Jia, Tao |u School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing 404100, China; taichen04@163.com (T.C.); chengcaijiang2003@163.com (C.J.); taojia2005@163.com (J.T.) | |
| 700 | 1 | |a Long, Ma |u School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing 404100, China; taichen04@163.com (T.C.); chengcaijiang2003@163.com (C.J.); taojia2005@163.com (J.T.) | |
| 700 | 1 | |a Cao Longzhou |u School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing 404100, China; taichen04@163.com (T.C.); chengcaijiang2003@163.com (C.J.); taojia2005@163.com (J.T.) | |
| 773 | 0 | |t Photonics |g vol. 12, no. 8 (2025), p. 801-813 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244049400/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244049400/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244049400/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |