Derives explicit approximation and generalization rates for multi-input neural operators in Sobolev spaces that quantify each input's contribution to the error.
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Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces
Derives explicit approximation and generalization rates for multi-input neural operators in Sobolev spaces that quantify each input's contribution to the error.