AI 中文总结
本文提出受生物尺度结构启发的几何可编程超材料梁作为物理储备池计算平台，其不同动力学区域在各类基准任务中性能优于线性梁，为具身物理智能提供了新的可调谐机械平台。
AI 中文摘要
物理储备池计算（PRC）利用物理系统的固有动力学实现信息处理，仅需对线性读出层进行训练。本文引入一种受生物尺度分层重叠结构启发的几何可编程超材料梁作为物理储备池，嵌入尺度间的接触介导相互作用可诱导可调谐非线性动力学，使其在外部激励下实现周期、多周期与混沌响应间的可控转变。该超材料梁储备池的计算能力通过静态非线性函数逼近、Lorenz-63预测及NARMA-2、NARMA-5、NARMA-10基准任务进行评估：输入信号通过激励幅度编码，计算则通过超材料梁的振动幅度结合训练后的线性读出实现。在所有基准任务中，所提出的超材料梁均优于等效线性梁储备池，表现为更低的归一化均方根误差（NRMSE）与更优的计算性能。超材料梁的不同动力学区域展现出独特计算优势：周期区域对记忆密集型任务提供最高预测精度，多周期区域在静态非线性函数逼近中实现最佳性能。这些发现表明，简单的相互作用表面纹理可同时丰富储备池动力学与信息处理能力，确立了覆有尺度的超材料梁作为可调谐、机械可编程平台，用于具身物理智能与PRC。
英文摘要
Physical reservoir computing (PRC) leverages the intrinsic dynamics of physical systems to perform information processing while requiring training only in a linear readout layer. Here, we introduce a geometry-programmable metabeam inspired by the hierarchical overlapping architecture of biological scales as a physical reservoir. Contact-mediated interactions between embedded scales induce tunable nonlinear dynamics, enabling controlled transitions between periodic, multi-periodic, and chaotic responses under external excitation. The computational capability of the metabeam reservoir is evaluated using static nonlinear function approximation, Lorenz-63 prediction, and the NARMA-2, NARMA-5, and NARMA-10 benchmarks. Input signals are encoded through the excitation amplitude, whereas computation is realized through the vibrational amplitude of the metabeam followed by a trained linear readout. Across all benchmark tasks, the proposed metabeam consistently outperforms an equivalent linear beam reservoir, yielding lower normalized root-mean-square errors (NRMSEs) and improved computational performance. Different dynamical regimes of the metabeam exhibit distinct computational advantages, with the periodic regime providing the highest prediction accuracy for memory-intensive tasks and the multi-periodic regime achieving the best performance in static nonlinear function approximation. These findings demonstrate that simple interacting surface textures can simultaneously enrich reservoir dynamics and information processing capability, establishing scale-covered metabeams as tunable and mechanically programmable platforms for embodied physical intelligence and PRC.
Comments22 pages, 9 figures
