The stochastic counterpart of conservation laws with heterogeneous conductivity fields: application to deterministic problems and uncertainty quantification

TL;DR

This paper introduces a new algorithm for solving one-dimensional stochastic elliptic PDEs with heterogeneous conductivity fields, overcoming limitations of existing Monte Carlo methods, and enabling efficient uncertainty quantification.

Contribution

The paper presents a novel algorithm that bypasses small time step restrictions and handles piecewise constant conductivities, improving stochastic PDE solution efficiency.

Findings

The method efficiently computes statistical moments of solutions.

It is applicable to deterministic and stochastic PDEs.

A variance reduction scheme enhances mean calculation efficiency.

Abstract

Conservation laws in the form of elliptic and parabolic partial differential equations (PDEs) are fundamental to the modeling of many problems such as heat transfer and flow in porous media. Many of such PDEs are stochastic due to the presence of uncertainty in the conductivity field. Based on the relation between stochastic diffusion processes and PDEs, Monte Carlo (MC) methods are available to solve these PDEs. These methods are especially relevant for cases where we are interested in the solution in a small subset of the domain. The existing MC methods based on the stochastic formulation require restrictively small time steps for high variance conductivity fields. Moreover, in many applications the conductivity is piecewise constant and the existing methods are not readily applicable in these cases. Here we provide an algorithm to solve one-dimensional elliptic problems that bypasses these two limitations. The methodology is demonstrated using problems governed by deterministic and stochastic PDEs. It is shown that the method provides an efficient alternative to compute the statistical moments of the solution to a stochastic PDE at any point in the domain. A variance reduction scheme is proposed for applying the method for efficient mean calculations.