Laplacian eigenfunction-based neural operators approximate the solution operator of the generalized Gierer-Meinhardt reaction-diffusion system with error bounds that imply only polynomial growth in parameters as accuracy improves.
Neural operator: Graph kernel network for partial differential equations
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
GRIFDIR proposes graph resolution-invariant FEM diffusion models that maintain resolution invariance and high fidelity on complex irregular domains.
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Approximation Theory of Laplacian-Based Neural Operators for Reaction-Diffusion System
Laplacian eigenfunction-based neural operators approximate the solution operator of the generalized Gierer-Meinhardt reaction-diffusion system with error bounds that imply only polynomial growth in parameters as accuracy improves.
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GRIFDIR: Graph Resolution-Invariant FEM Diffusion Models in Function Spaces over Irregular Domains
GRIFDIR proposes graph resolution-invariant FEM diffusion models that maintain resolution invariance and high fidelity on complex irregular domains.