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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A cyclic-orbit decomposition of the Leibniz formula yields sign laws, a rectification theorem, and a proof that no fixed-width Sarrus-style rule exists for n greater than or equal to 4.
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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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ARE Method: Orbital Decompositions and Dihedral Cancellations for Determinants
A cyclic-orbit decomposition of the Leibniz formula yields sign laws, a rectification theorem, and a proof that no fixed-width Sarrus-style rule exists for n greater than or equal to 4.