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Presenter: Yifeng Bie
Supervisor:

Date: Sat, September 12, 2026
Time: 10:00:00 - 00:00:00
Place: Zoom - see below.

ABSTRACT

Zoom Meeting Link:

https://uvic.zoom.us/j/8472200636?pwd=NTVCbHhZcnJYSU80VXlkSGlsMlF3Zz09&omn=83498705938

Meeting ID: 847 220 0636

Password: 700022

Note: Please log in to Zoom via SSO and your UVic Netlink ID

Abstract: 

 This work develops an unsupervised-learning framework for tracking whispering-gallery modes in a waveguide–microdisk coupled system. Instead of relying on computationally expensive full-field overlap calculations, each eigenmode is represented using a compact set of physically interpretable features. A hierarchical procedure is used to first separate guided and radiative modes, then classify the guided modes by polarization, and finally identify mode families across different geometric configurations using a reference-based tracking strategy.

The method is evaluated on a COMSOL dataset containing 3,000 eigenmodes from 40 geometric configurations. The proposed Gap-Reference approach achieves an average accuracy of 94.4% and a weighted F1-score of 92.8%, outperforming both K-means and agglomerative clustering. These results show that preserving modal continuity across a parameter sweep is more effective for mode tracking than treating the problem as conventional global clustering.