Mitigating the Influence of the Boundary on PDE-based Covariance Operators

TL;DR

This paper introduces two techniques to reduce boundary effects in PDE-based covariance operators for Gaussian fields, improving accuracy in large-scale spatial modeling.

Contribution

It proposes novel boundary mitigation methods combining Robin boundary conditions and variance normalization for PDE-based covariance operators.

Findings

Robin boundary approach reduces boundary artifacts

Variance normalization improves field consistency

Methods are effective in 2D and 3D complex domains

Abstract

Gaussian random fields over infinite-dimensional Hilbert spaces require the definition of appropriate covariance operators. The use of elliptic PDE operators to construct covariance operators allows to build on fast PDE solvers for manipulations with the resulting covariance and precision operators. However, PDE operators require a choice of boundary conditions, and this choice can have a strong and usually undesired influence on the Gaussian random field. We propose two techniques that allow to ameliorate these boundary effects for large-scale problems. The first approach combines the elliptic PDE operator with a Robin boundary condition, where a varying Robin coefficient is computed from an optimization problem. The second approach normalizes the pointwise variance by rescaling the covariance operator. These approaches can be used individually or can be combined. We study properties of these approaches, and discuss their computational complexity. The performance of our approaches is studied for random fields defined over simple and complex two- and three-dimensional domains.