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

ArXiv CS.AI2026-06-09NEWSen作者: Yijie Li, Jiahao Xu, Ching-Chih Tsao, Lili Qiu, Jingxian Wang

详细信息

来源站点
ArXiv CS.AI
作者
Yijie Li, Jiahao Xu, Ching-Chih Tsao, Lili Qiu, Jingxian Wang
文章类型
NEWS
语言
en
发布日期
2026-06-09

摘要

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. Yet existing broadband inverse-design approaches are either constrained by predefined templates, or rely on image representations that fail to preserve the geometric precision and structural connectivity required by acoustic structures. We present MetaSeq, a physics-guided, sequence-based generative framework for acoustic metamaterial inverse design. At its core, MetaSeq introduces a language that represents each AMM as a structured sequence, rather than as a pixel grid or fixed template.

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