Characterization of the second order random fields subject to linear distributional PDE constraints

TL;DR

This paper characterizes second order random fields satisfying linear PDE constraints in a distributional sense, with minimal regularity assumptions, and applies the results to Gaussian processes and the 3D wave equation.

Contribution

It provides a novel characterization of random fields satisfying linear PDEs in the distributional framework, extending previous results and enabling applications to Gaussian process regression.

Findings

Distributional PDE constraints can be characterized by moments.

Gaussian processes conditioned on observations satisfy PDE constraints.

Application to a 3D wave equation model.

Abstract

Let $L$ be a linear differential operator acting on functions defined over an open set $\mathcal{D}\subset \mathbb{R}^d$. In this article, we characterize the measurable second order random fields $U = (U(x))_{x\in\mathcal{D}}$ whose sample paths all verify the partial differential equation (PDE) $L(u) = 0$, solely in terms of their first two moments. When compared to previous similar results, the novelty lies in that the equality $L(u) = 0$ is understood in the sense of distributions, which is a powerful functional analysis framework mostly designed to study linear PDEs. This framework enables to reduce to the minimum the required differentiability assumptions over the first two moments of $(U(x))_{x\in\mathcal{D}}$ as well as over its sample paths in order to make sense of the PDE $L(U_{\omega})=0$. In view of Gaussian process regression (GPR) applications, we show that when $(U(x))_{x\in\mathcal{D}}$ is a Gaussian process (GP), the sample paths of $(U(x))_{x\in\mathcal{D}}$ conditioned on pointwise observations still verify the constraint $L(u)=0$ in the distributional sense. We finish by deriving a simple but instructive example, a GP model for the 3D linear wave equation, for which our theorem is applicable and where the previous results from the literature do not apply in general.