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Exploration of truss metamaterials with graph based generative modeling

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  • Angkur Jyoti Dipanka Shaikeea

    (University of Cambridge)

Abstract

In the expanding landscape of metamaterial design, Zheng and colleagues introduces a framework that bridges design and properties, using machine learning to enhance truss metamaterials. A neural network creates an interpretable, low-dimensional space, empowering designers to tailor mechanical properties.

Suggested Citation

  • Angkur Jyoti Dipanka Shaikeea, 2023. "Exploration of truss metamaterials with graph based generative modeling," Nature Communications, Nature, vol. 14(1), pages 1-3, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43217-y
    DOI: 10.1038/s41467-023-43217-y
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    References listed on IDEAS

    as
    1. T. Bückmann & M. Thiel & M. Kadic & R. Schittny & M. Wegener, 2014. "An elasto-mechanical unfeelability cloak made of pentamode metamaterials," Nature Communications, Nature, vol. 5(1), pages 1-6, September.
    2. Li Zheng & Konstantinos Karapiperis & Siddhant Kumar & Dennis M. Kochmann, 2023. "Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
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    1. Li Zheng & Konstantinos Karapiperis & Siddhant Kumar & Dennis M. Kochmann, 2023. "Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
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