Applications of machine learning methods for photonics and non-Hermitian physics
Changyan Zhu · 2024
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.