SS-POD augments standard POD-Galerkin with a spectral-subspace partition and local POD to achieve lower out-of-sample error than either plain POD or pure spectral-Galerkin when only a handful of snapshots are available.
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Controlled benchmarks show INR architectures differ in whether weight reuse is source-specific or generic, with no architecture dominating all PDE and analytic cases.
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A spectral-subspace-augmented POD-Galerkin method for parametrized PDEs with limited snapshot data
SS-POD augments standard POD-Galerkin with a spectral-subspace partition and local POD to achieve lower out-of-sample error than either plain POD or pure spectral-Galerkin when only a handful of snapshots are available.
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Architecture Shapes Transfer Specificity in Implicit Neural Representations
Controlled benchmarks show INR architectures differ in whether weight reuse is source-specific or generic, with no architecture dominating all PDE and analytic cases.