Sensing Intelligence as a Trainable Metamaterial Property
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910249726771200 |
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| author | Na, Kyungmi Li, Yifei Yang, Xinyi Deng, Bolei |
| author_facet | Na, Kyungmi Li, Yifei Yang, Xinyi Deng, Bolei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23967 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sensing Intelligence as a Trainable Metamaterial Property Na, Kyungmi Li, Yifei Yang, Xinyi Deng, Bolei Neurons and Cognition Materials Science Artificial Intelligence Applied Physics 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. |
| title | Sensing Intelligence as a Trainable Metamaterial Property |
| topic | Neurons and Cognition Materials Science Artificial Intelligence Applied Physics |
| url | https://arxiv.org/abs/2605.23967 |