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
arXiv:2606. 09266v1 Announce Type: cross Abstract: Acoustic metamaterial (AMM) inverse design is particularly challenging for broadband target responses due to acoustic dispersion: a structure that matches the desired response at one frequency may deviate at others, and modifying geometry to improve one sub-band often perturbs neighboring sub-bands.
By Yijie Li, Jiahao Xu, Ching-Chih Tsao, Lili Qiu, Jingxian Wang
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
The paper presents a method for training single‑step neural surrogates that can handle wave‑scattering problems with tens of thousands of controllable variables. By dynamically generating training examples that highlight surrogate errors and using a replay dataset with normalization, the authors achieve a surrogate that accurately simulates two‑dimensional wave scattering for up to 41,772 variables and generalizes to over 3 million variables without retraining. The surrogate is applied to forward simulations and inverse design of freeform beam splitters and gradient‑index lenses, achieving speedups up to 26.5× compared to traditional FDTD methods.
By Charles Dove, Laura Waller
The paper presents a method for training single‑step neural surrogates that can handle wave‑scattering inverse problems with tens of thousands of controllable variables. By dynamically generating training examples through gradient ascent and using a replay dataset with normalization, the authors achieve a surrogate that accurately models two‑dimensional wave scattering for up to 41,772 variables and can generalize to over 3 million variables without retraining. The surrogate demonstrates comparable or better performance than traditional FDTD simulations for large‑scale forward simulations and inverse design of photonic devices, achieving speedups up to 26.5×.
arXiv:2608. 11860v1 Announce Type: cross Abstract: Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses, but spectrum-to-geometry mapping remains challenging due to non-uniqueness and fine geometric features.
By Waleed Waseer, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Shujaat Khan