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

In three linesTheoretical analysis of generalization guarantees for multi-input neural operators with error measured in Sobolev norms. Framework handles multiple input functions on different domains with varying dimensions and regularities. Approximation and generalization rates explicitly quantify each input space's contribution to the final error bound.

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