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      Unifying the design space of truss metamaterials by generative modeling

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          Abstract

          The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials -- truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metamaterials has remained highly limited and often heuristic, due to the vast, discrete design space and the lack of a comprehensive parameterization. We here present a graph-based deep learning generative framework, which combines a variational autoencoder and a property predictor, to construct a reduced, continuous latent representation covering an enormous range of trusses. This unified latent space allows for the fast generation of new designs through simple operations (e.g., traversing the latent space or interpolating between structures). We further demonstrate an optimization framework for the inverse design of trusses with customized properties, including exceptionally stiff, auxetic, and pentamode-like designs. This generative model can predict manufacturable (and counter-intuitive) designs with extreme target properties beyond the training domain.

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          Author and article information

          Journal
          26 June 2023
          Article
          2306.14773
          b9981524-18a8-48aa-b786-18b402db8661

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          cs.CE

          Applied computer science
          Applied computer science

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