Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations
This provides a theoretical foundation for efficient neural network solutions to PDEs in scientific computing, though it is incremental as it builds on existing operator network frameworks.
The paper tackles approximating the coefficient-to-solution map for elliptic PDEs using deep operator networks, achieving exponential convergence with network sizes scaling as O(|log(ε)|^κ) for accuracy ε.
We construct and analyze approximation rates of deep operator networks (ONets) between infinite-dimensional spaces that emulate with an exponential rate of convergence the coefficient-to-solution map of elliptic second-order partial differential equations. In particular, we consider problems set in $d$-dimensional periodic domains, $d=1, 2, \dots$, and with analytic right-hand sides and coefficients. Our analysis covers linear, elliptic second order divergence-form PDEs as, e.g., diffusion-reaction problems, parametric diffusion equations, and elliptic systems such as linear isotropic elastostatics in heterogeneous materials. We leverage the exponential convergence of spectral collocation methods for boundary value problems whose solutions are analytic. In the present periodic and analytic setting, this follows from classical elliptic regularity. Within the ONet branch and trunk construction of [Chen and Chen, 1993] and of [Lu et al., 2021], we show the existence of deep ONets which emulate the coefficient-to-solution map to a desired accuracy in the $H^1$ norm, uniformly over the coefficient set. We prove that the neural networks in the ONet have size $\mathcal{O}(\left|\log(\varepsilon)\right|^κ)$, where $\varepsilon>0$ is the approximation accuracy, for some $κ>0$ depending on the physical space dimension.