Uniform Approximation for Operator Learning on Compact Sets

Abstract

We study uniform approximation for operator learning on compact subsets of Banach spaces. We bound the recovery dimensions and the number of network parameters in terms of compact-set complexity, the target gauge, and recovery stability. For bounded linear operators with a separable domain, we characterize gauge approximability by coordinate decompositions with a common vanishing gauge and arbitrarily small additive errors. Their partial sums give a fixed family of nonlinear encoder--decoder pairs. We prove that nonlinear recovery with a common vanishing gauge and a rank bound depending only on the model dimension is equivalent to the uniform approximation property (UAP). We also obtain linear recovery estimates with the same rank bound and an operator version for the linear factors of nonlinear targets. For Barron operators taking values in a Banach space $Y$ of type $p\in(1,2]$ and admitting a Bochner-integrable density, sampling gives the rate $N^{-(1-1/p)}$ for the expected $L^p(\lambda;Y)$ error, where $\lambda$ is a probability measure on the input compact set. For fixed finite-dimensional factors and a class of H\"older maps, we obtain matching upper and lower parameter bounds at fixed sufficiently large depth. Classical sampling and truncation examples provide explicit recovery maps, and a nonlinear integral operator illustrates the parameter bounds.