Random Batch Methods for Discretized PDEs on Graphs

TL;DR

This paper introduces a randomized computational method combining discretization and the Random Batch Method (RBM) to efficiently solve PDEs on graphs, demonstrating convergence and cost reduction for heat equations and potential for broader PDEs.

Contribution

The paper develops and analyzes a discretize+RBM approach for PDEs on graphs, proving convergence and extending to optimal control, with demonstrated computational efficiency.

Findings

Convergence of the discretize+RBM method in expectation.

Significant reduction in computational costs demonstrated.

Method applicable to a wide range of linear PDEs on graphs.

Abstract

Gas transport and other complex real-world challenges often require solving and controlling partial differential equations (PDEs) defined on graph structures, which typically demand substantial memory and computational resources. The Random Batch Method (RBM) offers significant relief from these demands by enabling the simulation of large-scale systems with reduced computational cost. In this paper, we analyze the application of RBM for solving PDEs on one-dimensional graphs, specifically concentrating on the heat equation. Our approach involves a two-step process: initially discretizing the PDE to transform it into a finite-dimensional problem, followed by the application of the RBM. We refer to this integrated approach as discretize+RBM. We establish the convergence of this method in expectation, under the appropriate selection and simultaneous reduction of the switching parameter in RBM and the discretization parameters. Moreover, we extend these findings to include the optimal control of the heat equation on graphs, enhancing the practical utility of our methodology. The efficacy and computational efficiency of our proposed solution are corroborated through numerical experiments that not only demonstrate convergence but also show significant reductions in computational costs. Our algorithm can be viewed as a randomized variant of domain decomposition, specifically adapted for PDEs defined on graph structures. It is sufficiently versatile to be applied to a wide range of linear PDEs -- not just the heat equation -- while maintaining comparable analytical guarantees and convergence properties.