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...
Main Author: | Zhu, Changyan |
---|---|
Other Authors: | Chong Yidong |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/173955 |
Similar Items
-
Topological bound states in the continuum in a non-Hermitian photonic system
by: Luo Yihao, et al.
Published: (2025-01-01) -
Identifying topology of leaky photonic lattices with machine learning
by: Smolina Ekaterina, et al.
Published: (2024-01-01) -
Topological phases and non-Hermitian topology in photonic artificial microstructures
by: Liu Hui, et al.
Published: (2023-02-01) -
Advances and applications on non-Hermitian topological photonics
by: Yan Qiuchen, et al.
Published: (2023-03-01) -
Asymmetric diffraction in anti-parity-time symmetry of non-Hermitian photonic lattice
by: Runrun Li, et al.
Published: (2024-09-01)