The paper presents a conditional diffusion framework for the inverse design of dielectric resonator metasurfaces based on target angular scattering patterns. Trained on T‑matrix simulated geometry‑response pairs, the model learns a distribution of feasible geometries, allowing multiple candidate designs for the ill‑posed inverse problem. The best generated metasurface achieves a mean percentage error of 1.39%, outperforming CMA‑ES optimization and deterministic neural baselines, and can be inferred in about one minute after training.
By M. Tsukerman, K. Grotov, D. Vovchuk, P. Ginzburg
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.
arXiv:2607. 24777v1 Announce Type: new Abstract: Architected metamaterials derive their functions from structure, creating vast opportunities to program physical responses through topology design.
By Haolin Li, Yuyang Miao, Menglei Li, Jinshuai Bai, Liyuan Wang, Xin Liu, Bo Gao, Jiantao Liu, Danilo Mandic, Zahra Sharif Khodaei, M. H. Aliabadi, Weiqiu Chen
The paper introduces a unified large language model workflow for modeling and inverse-design of metasurfaces across multiple families. By converting geometries, design parameters, and optical responses into a shared instruction‑following text format, the authors fine‑tune Gemma‑2‑9B on eight distinct metasurface families. Compared to single‑family models, the joint model predicts all families’ optical responses simultaneously and reduces mean‑squared error by an average of 56.5%.
"whyItMatters":"The approach demonstrates that a shared sequence‑based LLM interface can streamline cross‑family metasurface design, eliminating the need for separate surrogate architectures for each geometry class."
By Huanshu Zhang, Lei Kang, Yuyan Chen, Luxiang Wang, Zhaolong Cao, Douglas H. Werner
arXiv:2608.23469v1 Announce Type: cross
Abstract: A machine learning-assisted framework for the inverse design of pixelated millimetre-wave patch antennas targeting the 22--30 GHz band is presented....
By Nadeem Rather, Holger Claussen, Lester Ho
arXiv:2607. 07682v1 Announce Type: new Abstract: The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces.
By Xiangming Huang, Guannan Zhang, Lu Lu, Rapha\"el Pestourie