A learned interface-aware neural Newton preconditioner improves convergence on difficult CZM increments while preserving the original discrete solution set and force-displacement response.
Li et al.Neural Preconditioning Operator for Efficient PDE Solves
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The Neural Green's Operator matches exact coarse-solve iteration counts in two-level preconditioners for diffusion and advection-diffusion problems when inputs are integrated against the output basis.
citing papers explorer
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Interface-Aware Neural Newton Preconditioning for Robust Cohesive Zone Model Simulations
A learned interface-aware neural Newton preconditioner improves convergence on difficult CZM increments while preserving the original discrete solution set and force-displacement response.
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When can a neural operator replace a coarse solve? Architectural principles for two-level preconditioning
The Neural Green's Operator matches exact coarse-solve iteration counts in two-level preconditioners for diffusion and advection-diffusion problems when inputs are integrated against the output basis.