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Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces

arXiv cs.LG · 2d ago Cached

This paper provides approximation and generalization error estimates for multi-input neural operators measured in Sobolev norms, analyzing how multiple input functions with different domains and regularities affect error bounds, applicable to PDE and scientific computing problems.

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