New universal operator approximation theorem for encoder-decoder architectures

Motivated by the rapidly growing field of mathematics for operator approximation with neural networks, we present a novel universal operator approximation theorem for broad classes of encoder-decoder architectures and a wide range of input and output spaces.

In this study, we focus on the approximation of continuous operators between infinite-dimensional normed or metric spaces in the topology of uniform convergence on compact sets.

Unlike standard results in the operator learning literature, we additionally investigate the case where the approximating sequence of encoder-decoder architectures can be chosen independently of the compact sets.

Taking a topological perspective, we point out that compact-set-independent approximation is a strictly stronger property in most relevant operator learning frameworks.

To establish our results, we introduce new approximation properties of input and output spaces tailored to encoder-decoder architectures.

These properties enable us to prove a universal operator approximation theorem ensuring uniform convergence on every compact subset of the input space.

Our results unify and extend existing universal operator approximation theorems for various encoder-decoder architectures, including classical DeepONets, BasisONets, MIONets, architectures based on frames and other related approaches.

A notable feature of our framework is that it also applies to metric spaces beyond the normed setting.

In particular, it allows the consideration of $p$-Wasserstein spaces of probability measures as input or output spaces, and Skorohod spaces of càdlàg functions as input spaces.

This generality also opens up potential applications in optimal transport.