4.8 Article

Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials

Journal

PHYSICAL REVIEW LETTERS
Volume 129, Issue 19, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.129.198003

Keywords

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Funding

  1. European Research Council [852587]
  2. European Research Council (ERC) [852587] Funding Source: European Research Council (ERC)

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This paper demonstrates that convolutional neural networks can learn to recognize the boundaries of combinatorial mechanical metamaterials, even with sparse training sets, and successfully generalize, opening up new possibilities for complex material design.
Combinatorial problems arising in puzzles, origami, and (meta)material design have rare sets of solutions, which define complex and sharply delineated boundaries in configuration space. These boundaries are difficult to capture with conventional statistical and numerical methods. Here we show that convolutional neural networks can learn to recognize these boundaries for combinatorial mechanical metamaterials, down to finest detail, despite using heavily undersampled training sets, and can successfully generalize. This suggests that the network infers the underlying combinatorial rules from the sparse training set, opening up new possibilities for complex design of (meta)materials.

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