Proposes residual-based physics-informed coarsening in multigrid GNNs to allocate capacity to high-activity regions for more stable solid mechanics surrogates.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Mask-Morph Graph U-Net morphs coarse graph hierarchies with barycentric parameterization and applies masked supervised pretraining to improve generalizability of hierarchical GNN surrogates for crashworthiness prediction on variable meshes.
citing papers explorer
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Physics-Informed Coarsening for Multigrid Graph Neural Surrogates
Proposes residual-based physics-informed coarsening in multigrid GNNs to allocate capacity to high-activity regions for more stable solid mechanics surrogates.
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Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation
Mask-Morph Graph U-Net morphs coarse graph hierarchies with barycentric parameterization and applies masked supervised pretraining to improve generalizability of hierarchical GNN surrogates for crashworthiness prediction on variable meshes.