让材料自身学会感知，通过训练优化结构提升传感效果。
Sensing Intelligence as a Trainable Metamaterial Property
- 用可微分仿真让神经网络反向训练材料结构以优化传感
- 实验与仿真中传感精度最高提升5倍，传感器数量减少近10倍
- 适合机器人、智能穿戴等需低功耗感知的硬件设计
在生物系统中，感知不仅由大脑完成：身体会形变、振动并过滤外部刺激，再转化为神经信号。而在工程系统中，这一处理负担主要由电子设备和计算承担，机械结构通常仅追求强度与稳定性。本文提出将感知智能作为可训练的材料属性。我们展示，通过优化超材料的几何结构，可将外部刺激重塑为更易被神经网络解读的内部信号。不同于手工设计物理预处理，我们通过可微分仿真，将感知损失反向传播至材料设计参数，让神经网络自主训练其自身结构。在多种数值与实验传感场景中，优化后的结构使传感准确率最高提升五倍，或使所需电子传感器数量减少近一个数量级。
原文摘要 · Abstract (English)
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. In engineered systems, this processing burden is placed largely on electronics and computation, while the mechanical body is usually designed only for strength and stability. Here, we present sensing intelligence as a trainable property of the body. We show that the geometry of a metamaterial can be optimized to reshape external stimuli into internal signals that are easier for a neural network to interpret. Rather than hand-designing this physical preprocessing, we let the neural network train its own body for sensing by backpropagating the sensing loss to the body's design parameters through differentiable simulation. Across numerical and experimental sensing scenarios, the optimized body improves sensing accuracy by up to fivefold or reduces the number of required electronic sensors by nearly an order of magnitude.
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