AI 中文总结
本研究将多径复杂性与非线性转化为优化资源，提出原位伴随优化协议，在含局域非线性缺陷的波混沌平台上实验验证，为复杂环境下的自适应无线通信等应用提供新途径。
AI 中文摘要
在基于波的信息处理中，复杂多径环境通常被规避，因为多次散射会产生大量干扰传播路径，模糊可控性并导致对扰动的极端敏感性。非线性机制的加入从根本上改变了波控制格局，它打破了支撑大多数波管理策略的叠加原理。在此，我们表明这两个看似阻碍的因素——多径复杂性和非线性——反而可被用作物理优化的关键资源。我们在包含单个局域非线性缺陷的波混沌平台中演示了一种原位伴随优化协议，其中系统本身执行梯度评估所需的前向和伴随传播。循环多径返回使波反复暴露于缺陷，从最小硬件产生具有许多路径介导自由度的丰富非线性输入输出映射。同时，合适的伴随激励仅通过测量即可直接提取灵敏度，无需数字孪生或常规数值反向传播。我们在由通过T型接头连接的不可公度同轴电缆构成的最小非线性多径平台上实验验证了该协议，其中一个接头装有二极管负载腔。我们的方法为在复杂、部分未知且常规建模不切实际的环境中实现自适应无线通信、成像和模拟智能开辟了一条途径。
英文摘要
Complex multipath environments are usually avoided in wave-based information processing because repeated scattering creates many interfering propagation paths, obscuring controllability and generating extreme sensitivity to perturbations. The addition of nonlinear mechanisms fundamentally alters the wave-control landscape by breaking the superposition principle that underpins most wave-management strategies. Here, we show that these two apparent impediments -- multipath complexity and nonlinearity -- can instead be harnessed as key resources for physical optimization. We demonstrate an in-situ adjoint optimization protocol in a wave-chaotic platform incorporating a single localized nonlinear defect, in which the system itself performs both the forward and the adjoint propagations required for gradient evaluation. Recurrent multipath returns repeatedly expose the wave to the defect, producing from a minimal hardware a rich nonlinear input-output map with many pathway-mediated degrees of freedom. At the same time, a suitable adjoint excitation enables direct extraction of the sensitivities from measurements alone, without a digital twin or conventional numerical backpropagation. We experimentally validate the protocol on a minimal nonlinear multipath platform composed of incommensurate coaxial cables connected via T-junctions, one of which hosts a diode-loaded cavity. Our approach opens a route to adaptive wireless communications, imaging and analog intelligence in complex, partially unknown environments where conventional modeling is impractical.
