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.
Iterative methods by space decomposition and subspace correction
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Pith papers citing it
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math.NA 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Smoothing iterations on finite element solutions in an enriched space produce superconvergent approximations for symmetric positive definite problems.
citing papers explorer
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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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Superconvergence in finite element method by smoothing
Smoothing iterations on finite element solutions in an enriched space produce superconvergent approximations for symmetric positive definite problems.