Q-Factor Evaluation Accelerated by a Deep Neural Network

Antenna inverse design has gained significant popularity, yet it often faces challenges with slow convergence to optimal solutions. This paper presents a novel deep-learning algorithm for accelerated Q-factor evaluation based on the Method-of-Moments (MoM). The proposed deep neural network directly processes triangular antenna meshes to estimate the vector of current expansion coefficients, effectively replacing the traditional impedance matrix inversion required in MoM. The Q-factor is subsequently computed. For this purpose, we introduce QfResNET, a Residual Network (ResNET) architecture featuring an upsampling convolutional network and the GELU activation function. Antennas are represented as multi-channel 2D arrays, preserving the neighborhood relationships in the antenna mesh and labeled with Q-factors derived through MoM calculations. Our results demonstrate that QfResNET achieves a speedup of up to 957% compared to MoM. However, for coarser antenna meshes, the deep neural network does not offer a significant advantage.