arXiv AI By Kyungmi Na, Yifei Li, Xinyi Yang, Bolei Deng

Sensing Intelligence as a Trainable Metamaterial Property

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arXiv:2605. 23967v2 Announce Type: replace-cross Abstract: In biological systems, sensing is not performed by the brain alone: the body deforms, vibrates, and filters external stimuli before they are transduced into neural signals.

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arXiv Machine Learning
Jul 7

CertMix: Certified, Data-Efficient Metamaterial Design by Affine Mixing of Aligned Neural-Implicit Weight Spaces

arXiv:2607. 04123v1 Announce Type: new Abstract: Inverse design of mechanical metamaterials seeks a periodic unit cell whose homogenized elastic properties meet a prescribed target, but current learning-based methods are data-hungry, mostly interpolative, and provide no guarantee that the generated design satisfies the specification.

By Yifan Wang
arXiv AI
Jun 9

Physics-Guided Sequence-Based Generative Framework for Acoustic Metamaterial Inverse Design

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 Machine Learning
Sep 1

Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments

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 Machine Learning
Jul 21

Harnessing disorder to decouple extension and shear in kirigami metamaterials

arXiv:2607. 16583v1 Announce Type: cross Abstract: Kirigami turns stiff sheets into compliant, shape-morphing structures, but its reliance on periodic cut patterns comes at a cost: correlated panel rotations couple extension to shear, so stretching one axis drives a parasitic shear that cannot be suppressed, and also confine anisotropic stiffness to a narrow, discrete set of responses that cannot be tuned independently.

By Haomin Yu, Hanxun Jin, Mingxuan Bi, Mohammad Jafari, Feng Helen Long, Michael J Greenberg, Farid Alisafaei, Guy Genin
arXiv AI
Jul 13

A Self-Evolving Agentic Framework for Metasurface Inverse Design

arXiv:2604. 01480v2 Announce Type: replace Abstract: Metasurface inverse design can realize complex optical functionality, but turning a target optical response into executable optimization code still requires substantial expertise in computational electromagnetics and solver-specific software engineering.

By Yi Huang, Bowen Zheng, Yunxi Dong, Hong Tang, Huan Zhao, S. M. Rakibul Hasan Shawon, Hualiang Zhang