Graph Kernel Networks learn PDE solution operators that generalize across discretization methods and grid resolutions using graph-based kernel integration.
A multiscale neural network based on hierarchical matrices
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The paper establishes rigorous theorems proving the Frequency Principle holds for general deep neural networks at initial, intermediate, and final training stages.
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Neural Operator: Graph Kernel Network for Partial Differential Equations
Graph Kernel Networks learn PDE solution operators that generalize across discretization methods and grid resolutions using graph-based kernel integration.
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Theory of the Frequency Principle for General Deep Neural Networks
The paper establishes rigorous theorems proving the Frequency Principle holds for general deep neural networks at initial, intermediate, and final training stages.