Resonant Mode Recognition with Convolutional Neural Network

This research proposes a simple approach to recognize resonate modes in rectangular dielectric resonator antennas (DRAs) with machine learning (ML) techniques. The proposed method utilizes a convolutional neural network (CNN) with a focal loss function to classify various modes present in the antenna. DRAs with different dimensions are simulated with ANSYS HFSS to obtain their electric fields at various resonant frequencies. The resulting dataset comprises 700 fields, manually labeled according to their corresponding resonant modes. After training the CNN model using this dataset, it is capable of accurately classifying 12 low-order resonant modes in rectangular DRAs. The performance of the model is evaluated using a test set, where it achieves a recognition accuracy of 97.8%.