From Monte Carlo to neural networks approximations of boundary value problems

TL;DR

This paper demonstrates efficient Monte Carlo and neural network methods for approximating solutions to the Poisson equation in high dimensions, with guarantees on accuracy and complexity.

Contribution

It introduces a modified walk on spheres Monte Carlo algorithm and constructs neural network solutions with polynomial complexity for the Poisson problem.

Findings

Monte Carlo methods approximate solutions efficiently in the sup-norm.

Neural networks can be constructed with size polynomial in dimension and error.

The approach is dimension-robust and computationally feasible.

Abstract

In this paper we study probabilistic and neural network approximations for solutions to Poisson equation subject to Holder data in general bounded domains of $\mathbb{R}^d$. We aim at two fundamental goals. The first, and the most important, we show that the solution to Poisson equation can be numerically approximated in the sup-norm by Monte Carlo methods, and that this can be done highly efficiently if we use a modified version of the walk on spheres algorithm as an acceleration method. This provides estimates which are efficient with respect to the prescribed approximation error and with polynomial complexity in the dimension and the reciprocal of the error. A crucial feature is that the overall number of samples does not not depend on the point at which the approximation is performed. As a second goal, we show that the obtained Monte Carlo solver renders in a constructive way ReLU deep neural network (DNN) solutions to Poisson problem, whose sizes depend at most polynomialy in the dimension $d$ and in the desired error. In fact we show that the random DNN provides with high probability a small approximation error and low polynomial complexity in the dimension.