Space-time error estimates for deep neural network approximations for differential equations

TL;DR

This paper establishes space-time error estimates for deep neural network approximations of solutions to perturbed differential equations, advancing the mathematical understanding of neural network methods for PDEs.

Contribution

It provides the first rigorous space-time error estimates for DNN approximations of PDE solutions, including the development of new neural network calculus and approximation results.

Findings

Derived space-time error bounds for neural network PDE approximations

Developed neural network calculus for error analysis

Established approximation results for neural networks on product spaces

Abstract

Over the last few years deep artificial neural networks (DNNs) have very successfully been used in numerical simulations for a wide variety of computational problems including computer vision, image classification, speech recognition, natural language processing, as well as computational advertisement. In addition, it has recently been proposed to approximate solutions of partial differential equations (PDEs) by means of stochastic learning problems involving DNNs. There are now also a few rigorous mathematical results in the scientific literature which provide error estimates for such deep learning based approximation methods for PDEs. All of these articles provide spatial error estimates for neural network approximations for PDEs but do not provide error estimates for the entire space-time error for the considered neural network approximations. It is the subject of the main result of this article to provide space-time error estimates for DNN approximations of Euler approximations of certain perturbed differential equations. Our proof of this result is based (i) on a certain artificial neural network (ANN) calculus and (ii) on ANN approximation results for products of the form $[0,T]\times \mathbb{R}^d\ni (t,x)\mapsto tx\in \mathbb{R}^d$ where $T\in (0,\infty)$, $d\in \mathbb{N}$, which we both develop within this article.