Applications of machine learning methods for photonics and non-Hermitian physics
The recent advances in machine learning and related techniques have arisen their application in different areas. In physics, especially in photonics, Machine learning learns from the dataset and provide an accurate description of mapping between different physical variables. Therefore, they are qui...
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| Định dạng: | Thesis-Doctor of Philosophy |
| Ngôn ngữ: | English |
| Được phát hành: |
Nanyang Technological University
2024
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| Những chủ đề: | |
| Truy cập trực tuyến: | https://hdl.handle.net/10356/173955 |
| _version_ | 1826128648067874816 |
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| author | Zhu, Changyan |
| author2 | Chong Yidong |
| author_facet | Chong Yidong Zhu, Changyan |
| author_sort | Zhu, Changyan |
| collection | NTU |
| description | The recent advances in machine learning and related techniques have arisen their application in different areas. In physics, especially in photonics, Machine learning learns from the dataset and provide an accurate description of mapping between different physical variables. Therefore, they are quite powerful in physics research. This thesis explores various machine learning algorithms for photonics and non-Hermitian physics.
Chapter 1 introduces the interplay between machine learning and physics. In particular, dense neural networks and convolutional neural networks are explained in detail.
Chapter 2 compares the efficiency of dense neural network (DNN), U-Net, and VGG-net for multi-mode fiber (MMF) image reconstruction, with DNN emerging as the most suitable due to its ability to consider non-local features.
Chapter 3 introduces an intelligent real-time and self-adaptive terahertz beamforming scheme based on neural networks, demonstrating accurate beam steering and high generalizability.
Chapter 4 uses a neural network with regularization for the reconstruction of random spectrometer signals, outperforming traditional matrix inversion methods in terms of bandwidth and accuracy. A compact, tunable spectrometer is also developed.
Chapter 5 extends the exploration to non-Hermitian physics, identifying topological variants and Non-Hermitian Skin Effects (NHSE) using unsupervised learning methods. The potential applications of NHSE in quantum amplifiers and the continuum of bound states in non-Hermitian lattices are also discussed.
Chapter 6 summarizes the entire thesis and discusses potential directions for future research based on the current thesis. |
| first_indexed | 2024-10-01T07:28:03Z |
| format | Thesis-Doctor of Philosophy |
| id | ntu-10356/173955 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:28:03Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1739552024-04-09T03:58:58Z Applications of machine learning methods for photonics and non-Hermitian physics Zhu, Changyan Chong Yidong School of Physical and Mathematical Sciences Centre for Disruptive Photonic Technologies (CDPT) Yidong@ntu.edu.sg Physics Photonics Non-Hermitian Machine learning The recent advances in machine learning and related techniques have arisen their application in different areas. In physics, especially in photonics, Machine learning learns from the dataset and provide an accurate description of mapping between different physical variables. Therefore, they are quite powerful in physics research. This thesis explores various machine learning algorithms for photonics and non-Hermitian physics. Chapter 1 introduces the interplay between machine learning and physics. In particular, dense neural networks and convolutional neural networks are explained in detail. Chapter 2 compares the efficiency of dense neural network (DNN), U-Net, and VGG-net for multi-mode fiber (MMF) image reconstruction, with DNN emerging as the most suitable due to its ability to consider non-local features. Chapter 3 introduces an intelligent real-time and self-adaptive terahertz beamforming scheme based on neural networks, demonstrating accurate beam steering and high generalizability. Chapter 4 uses a neural network with regularization for the reconstruction of random spectrometer signals, outperforming traditional matrix inversion methods in terms of bandwidth and accuracy. A compact, tunable spectrometer is also developed. Chapter 5 extends the exploration to non-Hermitian physics, identifying topological variants and Non-Hermitian Skin Effects (NHSE) using unsupervised learning methods. The potential applications of NHSE in quantum amplifiers and the continuum of bound states in non-Hermitian lattices are also discussed. Chapter 6 summarizes the entire thesis and discusses potential directions for future research based on the current thesis. Doctor of Philosophy 2024-03-08T00:45:00Z 2024-03-08T00:45:00Z 2024 Thesis-Doctor of Philosophy Zhu, C. (2024). Applications of machine learning methods for photonics and non-Hermitian physics. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173955 https://hdl.handle.net/10356/173955 10.32657/10356/173955 en MOE2016-T3- 1-006 RG187/18 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Physics Photonics Non-Hermitian Machine learning Zhu, Changyan Applications of machine learning methods for photonics and non-Hermitian physics |
| title | Applications of machine learning methods for photonics and non-Hermitian physics |
| title_full | Applications of machine learning methods for photonics and non-Hermitian physics |
| title_fullStr | Applications of machine learning methods for photonics and non-Hermitian physics |
| title_full_unstemmed | Applications of machine learning methods for photonics and non-Hermitian physics |
| title_short | Applications of machine learning methods for photonics and non-Hermitian physics |
| title_sort | applications of machine learning methods for photonics and non hermitian physics |
| topic | Physics Photonics Non-Hermitian Machine learning |
| url | https://hdl.handle.net/10356/173955 |
| work_keys_str_mv | AT zhuchangyan applicationsofmachinelearningmethodsforphotonicsandnonhermitianphysics |