Inverse Design of Ultra-Wideband Frequency Selective Surface Using a Graph based Conditional Variational Autoencoder (G-CVAE) integrated with a Physics Informed Neural Network (PINN) - V, Bharathi; Ramanujam, Krishnamurthy; Ramanujam, Parthasarathy (IEEE journal on multiscale and multiphysics computational techniques, 2025)
This work proposes the inverse design of bandstop Frequency Selective Surface using a Graph based Conditional Variational Autoencoder (G-CVAE) integrated with a Physics-Informed Neural Network (PINN). This inverse design involves the prediction of FSS geometry that exhibits ultra-wide stopband characteristics. Initially, the graph convolutional network precisely extracts the topological and spatial relationships within the FSS geometrical design. The features of the graph and simulation results of the FSS dataset are used to train the CVAE, which maps the FSS physical structure and its electromagnetic behavior. The trained CVAE predicts the FSS geometries with desired frequency responses, while the PINN is incorporated to ensure physical feasibility. By monitoring the average relative error values, the simulated and predicted transmission coefficients are brought closer to each other. Also, similar approach is followed to enhance the angular stability and to achieve polarization independence in both TE and TM modes. A G-CVAE-PINN is constructed and trained using various random combinations of graph attributes and simulation outcomes, achieving an average inaccuracy of 3%. Further, one of the best designs from the predicted FSS designs is chosen for experimental validation. This predicted and experimentally validated bandstop FSS exhibits wide band rejection of 20 GHz ranging from 8 GHz to 28 GHz. The fabricated design exhibits polarization independence up to 75°in both normal and oblique angles. Thus, the predicted FSS designs are ideal for radome, EMI shielding, and satellite communications, providing efficient frequency filtering for 5G and beyond 5G networks.