Recent Advances in Physics-Driven Machine Learning Approaches to Intelligent Design of Metasurfaces
- Resource Type
- Conference
- Authors
- Lin, Xiumei; Hou, Junming; Zhang, Jianan; You, Jian Wei; Cui, Tie Jun
- Source
- 2023 Photonics & Electromagnetics Research Symposium (PIERS) Photonics & Electromagnetics Research Symposium (PIERS), 2023. :1212-1216 Jul, 2023
- Subject
- Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Electric potential
Design methodology
Electromagnetic scattering
Machine learning
Predictive models
Metasurfaces
Microwave theory and techniques
- Language
- ISSN
- 2831-5804
Metasurfaces are widely used to manipulate electromagnetic (EM) waves. Traditional methods to metasurfaces design demand extensive full-wave EM simulations. Machine-learning-based approaches have been used to create fast and intelligent metasurfaces design. However, most of these approaches are data-driven methods that require a vast number of data produced by full-wave EM simulations. Physics-driven machine learning approaches can achieve higher accuracy and better physical interpretability than data-driven machine learning approaches. In this paper, we review the recent advances in physics-driven machine learning approaches that incorporate temporal coupled mode theory (CMT) especially the neuro-CMT model to improve the design efficiency of metasurfaces. Three examples of metasurface absorbers are provided to illustrate the benefits of this method.