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. This representation preserves precise geometry, explicitly encodes connectivity, and casts inverse design as a sequence-to-sequence task from target response to structure sequence. MetaSeq further constructs a balanced, high-fidelity dataset with efficient calibration and complexity-based sampling. To address the one-to-many nature of inverse design, MetaSeq combines supervised pretraining with reinforcement learning fine-tuning guided by a physics-based solver and validity checker. Extensive evaluations against COMSOL and five baselines show that MetaSeq reduces response error by 45% over the best baseline.

翻译：声学超材料逆向设计在宽带目标响应中面临特殊挑战，这源于声学色散效应：在某个频率匹配目标响应的结构可能在其它频率出现偏离，而调整几何形状优化某个子频段时常会扰动相邻子频段。现有宽带逆向设计方法要么受限于预定义模板，要么依赖无法保持声学结构所需几何精度和结构连通性的图像表示。我们提出MetaSeq——一种基于物理引导的序列生成框架用于声学超材料逆向设计。其核心创新在于引入了一种语言，将每个声学超材料表示为结构化序列而非像素网格或固定模板。这种表示方式既能保留精确几何信息，又能显式编码结构连通性，并将逆向设计转化为从目标响应到结构序列的序列到序列任务。MetaSeq进一步通过高效校准和基于复杂度的采样构建平衡的高保真数据集。针对逆向设计的一对多特性，MetaSeq结合了监督预训练与基于物理求解器和有效性校验器引导的强化学习微调。与COMSOL及五种基线方法的广泛评估表明，MetaSeq相比最优基线将响应误差降低了45%。