Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials. (arXiv:2401.16914v1 [cs.LG])

Lattices are architected metamaterials whose properties strongly depend on
their geometrical design. The analogy between lattices and graphs enables the
use of graph neural networks (GNNs) as a faster surrogate model compared to
traditional methods such as finite element modelling. In this work we present a
higher-order GNN model trained to predict the fourth-order stiffness tensor of
periodic strut-based lattices. The key features of the model are (i) SE(3)
equivariance, and (ii) consistency with the thermodynamic law of conservation
of …

analogy arxiv cs.lg design elasticity element energy faster gnn gnns graph graph neural networks graphs lattice modelling networks neural networks work