A Deep Learning Framework for Multi-Operator Learning: Architectures and Approximation Theory
This work provides a foundational framework for scalable neural operator learning, addressing a key challenge in scientific machine learning for applications like parametric PDEs.
The paper tackles the problem of learning collections of operators, such as mappings between function spaces in scientific applications, by introducing new architectures and providing theoretical approximation results. It demonstrates strong expressive power and efficiency in empirical tests on parametric PDE benchmarks.
While many problems in machine learning focus on learning mappings between finite-dimensional spaces, scientific applications require approximating mappings between function spaces, i.e., operators. We study the problem of learning collections of operators and provide both theoretical and empirical advances. We distinguish between two regimes: (i) multiple operator learning, where a single network represents a continuum of operators parameterized by a parametric function, and (ii) learning several distinct single operators, where each operator is learned independently. For the multiple operator case, we introduce two new architectures, $\mathrm{MNO}$ and $\mathrm{MONet}$, and establish universal approximation results in three settings: continuous, integrable, or Lipschitz operators. For the latter, we further derive explicit scaling laws that quantify how the network size must grow to achieve a target approximation accuracy. For learning several single operators, we develop a framework for balancing architectural complexity across subnetworks and show how approximation order determines computational efficiency. Empirical experiments on parametric PDE benchmarks confirm the strong expressive power and efficiency of the proposed architectures. Overall, this work establishes a unified theoretical and practical foundation for scalable neural operator learning across multiple operators.