Machine Learning for Topological Photonics
Program:
Applied Mathematics and Statistics
Project Description:
The integration of topology into photonics has sparked a revolution, leading to significant advancements in areas such as integrated optics and laser technology. As the ambition to create large-scale topological photonic devices grows, so does the complexity of designing these systems. A central challenge in this endeavor is the inverse problem: determining the ideal topology that achieves desired wave frequencies, particularly in ensuring the presence of protected edge states. Our project aims to address this challenge directly by adopting the transfer matrix method to solve the direct problem of topological optics—identifying wave frequencies that correspond to edge states given certain topological parameters.
We turn to the inverse problem, to ascertain the optimal combination of materials in a topological insulator. Our methodology marks a significant advancement in topological photonics by offering a practical and effective strategy for designing photonic devices with custom functionalities.
Team Members
-
[foreach 357]
-
[if 397 not_equal=””][/if 397][395]
[/foreach 357]
Project Mentors, Sponsors, and Partners
-
Fadil Santosa
Course Faculty
-
[foreach 429]
-
[if 433 not_equal=””][/if 433][431]
[/foreach 429]
