# Sparse Monte Carlo method for nonlocal diffusion problems

## TL;DR

This paper introduces a sparse Monte Carlo combined with discontinuous Galerkin method for efficiently solving nonlocal diffusion equations, proving convergence and analyzing error sources, with numerical validation.

## Contribution

It presents a novel sparse sampling approach for nonlocal diffusion problems, reducing computational cost while maintaining accuracy, with rigorous convergence analysis.

## Key findings

- Sparse Monte Carlo method reduces the number of discretization points needed.
- Convergence of the proposed numerical scheme is proven and error estimates are provided.
- Numerical experiments confirm the theoretical convergence rates.

## Abstract

A class of evolution equations with nonlocal diffusion is considered in this work. These are integro-differential equations arising as models of propagation phenomena in continuum media with nonlocal interactions including neural tissue, porous media flow, peridynamics, models with fractional diffusion, as well as continuum limits of interacting dynamical systems. The principal challenge of numerical integration of nonlocal systems stems from the lack of spatial regularity of the data and solutions intrinsic to nonlocal models. To overcome this problem we propose a semidiscrete numerical scheme based on the combination of sparse Monte Carlo and discontinuous Galerkin methods. An important feature of our method is sparsity. Sparse sampling of points in the Monte Carlo approximation of the nonlocal term allows to use fewer discretization points without compromising the accuracy. We prove convergence of the numerical method and estimate the rate of convergence. There are two principal ingredients in the error of the numerical method related to the use of Monte Calro and Galerkin approximations respectively. We analyze both errors. Two representative examples of discontinuous kernels are presented. The first example features a kernel with a singularity, while the kernel in the second example experiences jump discontinuity. We show how the information about the singularity in the former case and the geometry of the discontinuity set in the latter translate into the rate of convergence of the numerical procedure. In addition, we illustrate the rate of convergence estimate with a numerical example of an initial value problem, for which an explicit analytic solution is available. Numerical results are consistent with analytical estimates.