Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling.

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 fu

SUBMITTER: Zheng L 

PROVIDER: S-EPMC10663604 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature