Detection of Modal Numbers from Field Configurations in Rectangular Waveguides via Machine Learning Models of Noisy Datasets
Rasul Choupanzadeh,
Ata Zadehgol
Abstract:We propose a machine learning (ML) modeling methodology to predict the
propagation mode number of electromagnetic (EM) fields inside a metallic
rectangular waveguide based on the field configuration in the waveguide
cross-section, in the presence of noise. We consider the Transverse
Electric (TEmn) modes and assume m and n in the range of 0 to 2 inside
the waveguides, where the magnitude and phase of the noiseless field
configurations are obtained from the analytical solution of the electric
vector field E. We… Show more
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