Machine Learning for Rectangular Waveguide Mode Identification, Using 2D
Modal Field Patterns
Abstract
We apply machine learning (ML) techniques to identify the modes in
rectangular waveguides from images of 2D modal field patterns injected
with uniform, exponential, correlated exponential, and Gaussian noise
distributions. A binary classifier is used to identify either transverse
electric (TE) or transverse magnetic (TM) modes, and a Multi-class
classifier is used to identify the mode numbers. Signal to noise ratios
of 1, 0.1, and 0.01 are used to show the effectiveness of each model.
Results show accuracy scores up to 99.95%. Several examples demonstrate
that noisy modal patterns (unidentifiable to human eyes) may be
successfully classified by the ML model.