A framework learns boundary-to-domain pseudo-extensions to condition neural operators on complex BCs, achieving SOTA accuracy on 18 challenging PDE datasets without hyperparameter tuning.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Multigrid plus neural-network acceleration reduces the cost of phi-FEM while preserving accuracy in 2D and 3D test cases.
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Imposing Boundary Conditions on Neural Operators via Learned Function Extensions
A framework learns boundary-to-domain pseudo-extensions to condition neural operators on complex BCs, achieving SOTA accuracy on 18 challenging PDE datasets without hyperparameter tuning.
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A multigrid and neural network approach to reduce the computational cost of phi-FEM
Multigrid plus neural-network acceleration reduces the cost of phi-FEM while preserving accuracy in 2D and 3D test cases.