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)

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 chara...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal on multiscale and multiphysics computational techniques pp. 1 - 10
Main Authors: V, Bharathi, Ramanujam, Krishnamurthy, Ramanujam, Parthasarathy
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
5G and beyond 5G applications
Accuracy
Autoencoders
Computer architecture
Conditional Variational Autoencoder
Feature extraction
Frequency Selective Surface
Geometry
Inverse design
Microprocessors
Optimization
Physics-Informed Neural Network
Shielding Effectiveness
Training
Vectors
ISSN:2379-8815, 2379-8815
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2379-8815
2379-8815
DOI:10.1109/JMMCT.2025.3629980