A cross-attention-based bipartite GNN predicts coupled nodal displacement increments and elemental thinning directly on their native mesh domains for sheet material forming.
arXiv preprint arXiv:2309.10050 (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A GNN-based hybrid twin learns the ignorance component of physics simulations from sparse data and generalizes corrections across meshes, geometries, and loads in nonlinear heat transfer.
citing papers explorer
-
Cross-attention-based bipartite graph neural network for coupled nodal and elemental field prediction in large-deformation sheet material forming
A cross-attention-based bipartite GNN predicts coupled nodal displacement increments and elemental thinning directly on their native mesh domains for sheet material forming.
-
Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework
A GNN-based hybrid twin learns the ignorance component of physics simulations from sparse data and generalizes corrections across meshes, geometries, and loads in nonlinear heat transfer.