ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Mechanical Metamaterials for Handwritten Digits Recognition.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- In-memory phononic learning toward cognitive mechanical intelligence.Science advances · 2026Article
- Self-locking non-volatile coding metasurfaces via origami-based mechanical bits.Nature communications · 2026Article
- Spatially programmable origami networks enable high-density mechanical computing for autonomous robotics.Nature communications · 2025Article
- A kinematically Bifurcated Metamaterial for Integrated Logic Operation and Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Mechanical Neural Networks with Explicit and Robust Neurons.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Mechanical Metamaterials for Handwritten Digits Recognition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
The increasing needs for new types of computing lie in the requirements in harsh environments. In this study, the successful development of a non-electrical neural network is presented that functions based on mechanical computing. By overcoming the challenges of low mechanical signal transmission efficiency and intricate layout design methodologies, a mechanical neural network based on bistable kirigami-based mechanical metamaterials have designed. In preliminary tests, the system exhibits high reliability in recognizing handwritten digits and proves operable in low-temperature environments. This work paves the way for a new, alternative computing system with broad applications in areas where electricity is not accessible. By integrating with the traditional electronic computers, the present system lays the foundation for a more diversified form of computing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.