Inverse Design of Realizable Metasurface based Absorbers using Improved Conditioning and Diversity Enhanced Progressively Growing GANs

2026-06-04

AI Summary

This work addresses the limitations of conventional metasurface inverse design, which relies on time-consuming full-wave simulations and suffers from insufficient control accuracy and structural diversity in existing generative approaches. The authors propose a generative inverse design framework based on a progressively growing Wasserstein GAN, incorporating feature-wise linear modulation to achieve high-precision spectral conditioning. Physical consistency is ensured through a surrogate model–guided spectral alignment loss, while geometric diversity is enhanced via determinantal point process regularization. Evaluated over the 2–18 GHz band, the method achieves an average mean squared error of 0.0052, a diversity score of 0.8730, a band-alignment accuracy of 0.8533, and an effective design generation rate of 89.57%, significantly outperforming current state-of-the-art techniques.

Abstract

Metasurfaces enable precise manipulation of electromagnetic waves for applications such as beam steering, sensing, and stealth technology. However, inverse design of metasurfaces with targeted EM responses remains challenging due to the computational expense of iterative full wave simulation driven optimization and the limited conditioning fidelity and diversity of existing generative approaches. To address these challenges, this paper presents a generative inverse design framework for controllable and physically consistent metasurface synthesis under continuous spectral constraints. The proposed approach employs a progressively growing Wasserstein generative adversarial network with gradient penalty integrated with feature wise linear modulation based conditioning for stable propagation of continuous spectral and fabrication constraints. EM consistency is embedded directly into the generative learning process through a surrogate assisted spectral alignment loss, enabling physics constrained generation during training. Further, a determinantal point process based diversity regularization strategy is incorporated to generate geometrically diverse yet spectrally consistent realizations for the same target response. The effectiveness of the proposed framework is demonstrated through the generation of practically realizable metasurface absorbers exhibiting diverse reflection characteristics in the frequency range of 2 to 18 GHz. EM simulations validate that the generated designs meet the target specifications with high accuracy. The final proposed framework achieved an average mean squared error of 0.0052, diversity score of 0.8730, band alignment accuracy of 0.8533, and a valid EM design generation percentage of 89.57, clearly demonstrating its capability to generate highly accurate, diverse, electromagnetically consistent and fabrication realizable metasurface configurations.

Problem

• inverse design
• metasurface absorbers
• electromagnetic response
• generative models
• fabrication constraints

Innovation

• Inverse Design
• Metasurface Absorber
• Progressively Growing GAN
• Spectral Conditioning
• Diversity Regularization