… l1 and l2-based graph smoothing, we propose the following elastic graph signal estimator: …
(8) into deep neural networks, we introduce a family of GNNs, namely Elastic GNNs. In this …

… graph neural network [GNN]–elastic) based on the GNN architecture, which is capable of
implementing the elastic … as graph data and later processed by a modified graph isomorphism …

… applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response
… Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in …

… a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli
of … We use the Mapper algorithm to transform 3D digital rock images into graph datasets, …

… 2 Schematic overview of the MatTen graph neural network model. (A) The model takes a …
with equivariant graph neural network (GNN) layers, and outputs the full elasticity tensor …

… the full fourth-rank elasticity tensors of crystals. Based on equivariant graph neural networks,
MatTen satisfies the two essential requirements for elasticity tensors: independence of the …

… In summary, the max-pooled feature from the graph neural networks A encodes the material
information, and, together with the information of the DCV, the concatenated latent feature B …

… elastic net constraint into graph convolution definition and develop a novel graph elastic …
We can observe that GeCN generally outperforms other graph neural network models, …

… elastic material is utilized, stiffeners are evenly distributed, and uniform loading is applied on
straight panels. Two graph neural networks … , differing only in graph embedding. Illustration …

… and very deep neural networks—we propose a flexible architecture called Elastic Net, which
… Although attaching intermediate outputs to the network graph has been studied earlier, [8]–[…