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

📄 arXiv: 2606.05849v1 📥 PDF

作者: Vineetha Joy, Mohammad Abdullah, Pramit Pal, Anshuman Kumar, Amit Sethi, Hema Singh

分类: physics.optics, cs.CV

发布日期: 2026-06-04

💡 一句话要点

提出生成对抗网络以解决超材料吸收器逆向设计问题

🎯 匹配领域: 支柱一：机器人控制 (Robot Control)

关键词: 超材料设计 生成对抗网络 电磁波操控 逆向设计 多样性正则化

📋 核心要点

  1. 现有的超材料逆向设计方法在计算上成本高，且生成的设计缺乏多样性和物理一致性。
  2. 本文提出了一种基于渐进式生长的生成对抗网络，结合光谱约束和多样性正则化策略，提升设计的稳定性和多样性。
  3. 实验结果显示，生成的超材料吸收器在频率范围内展现出高准确度和多样性，平均均方误差为0.0052，设计有效率达到89.57%。

📝 摘要（中文）

超材料能够精确操控电磁波，广泛应用于波束转向、传感和隐身技术。然而，针对特定电磁响应的超材料逆向设计面临计算成本高和现有生成方法的条件保真度及多样性不足等挑战。为此，本文提出了一种生成逆向设计框架，能够在连续光谱约束下合成可控且物理一致的超材料。该方法采用渐进式生长的Wasserstein生成对抗网络，结合特征线性调制的条件设计，确保光谱和制造约束的稳定传播。通过代理辅助的光谱对齐损失，将电磁一致性直接嵌入生成学习过程中，确保训练期间的物理约束生成。实验结果表明，该框架生成的超材料吸收器在2至18 GHz频率范围内展现出多样的反射特性，且设计满足目标规格的准确率高达89.57%。

🔬 方法详解

问题定义：本文旨在解决超材料逆向设计中的高计算成本和现有生成方法在条件保真度及多样性方面的不足。

核心思路：通过引入渐进式生长的Wasserstein生成对抗网络，结合特征线性调制的条件设计，确保生成过程中的物理一致性和光谱约束。

技术框架：整体框架包括生成对抗网络的训练过程，采用渐进式生长策略逐步增加网络复杂度，同时引入光谱约束和多样性正则化模块。

关键创新：最重要的创新在于将电磁一致性直接嵌入生成学习过程，使用代理辅助的光谱对齐损失，确保生成设计的物理可实现性。

关键设计：关键参数包括渐进式生长的网络结构、特征线性调制的条件设计，以及基于行列式点过程的多样性正则化策略，这些设计确保了生成的超材料在光谱上具有一致性和几何多样性。

📊 实验亮点

实验结果表明，所提出的框架在生成超材料吸收器方面表现优异，平均均方误差为0.0052，设计有效率达到89.57%，显示出其在电磁一致性和几何多样性方面的显著优势。

🎯 应用场景

该研究的潜在应用领域包括电磁波操控、传感器设计、隐身技术等。通过提供一种高效的超材料设计方法，能够加速新型电磁设备的开发，推动相关技术的进步与应用。

📄 摘要（原文）

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.