NSPOD is a multigrid-like preconditioner using DeepONet-learned POD subspaces that dramatically cuts Krylov solver iterations for solid mechanics PDEs on unstructured CAD geometries, outperforming algebraic multigrid.
Weisfeiler and leman go neural: Higher-order graph neural networks
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A spectral-based GCN for directed graphs uses redefined Laplacians to enable direct application to directed data and outperforms prior methods on semi-supervised node classification tasks.
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NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
NSPOD is a multigrid-like preconditioner using DeepONet-learned POD subspaces that dramatically cuts Krylov solver iterations for solid mechanics PDEs on unstructured CAD geometries, outperforming algebraic multigrid.
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Spectral-based Graph Convolutional Network for Directed Graphs
A spectral-based GCN for directed graphs uses redefined Laplacians to enable direct application to directed data and outperforms prior methods on semi-supervised node classification tasks.