Pseudo-Differential Operators and Generalized Random Fields over Tori
Nicolas Escobar-Velasquez
TL;DR
The paper analyzes Matérn Gaussian random fields on the torus using a pseudo-differential operator framework, revealing a dimension-dependent threshold: on a $d$-torus, achieving local Hölder regularity $C^{(\nu-3d/2)^-}_{loc}$ requires $\nu > 3d/2$, unlike the Euclidean case. It employs Cardona-Martínez theory to establish regularity results and introduces the canonical-Matérn process, a three-parameter extension that attains $C^{(\nu-3d/2+2)^-}_{loc}$, gaining two orders of smoothness. The work also develops a canonical field with explicit dimension-dependent covariance behavior, provides a discretization scheme whose discrete model converges to the continuous limit, and proves an exponentially convergent convolution representation for the canonical-Matérn covariance, enabling efficient computation. Together, these results refine the understanding of spatial modeling on manifolds and offer a practical, geometry-aware alternative to standard Matérn models in periodic domains.
Abstract
Matérn covariance functions are ubiquitous in spatial statistics, valued for their interpretable parameters and well-understood sample path properties in Euclidean settings. This paper examines whether these desirable properties transfer to manifold domains through rigorous analysis of Matérn processes on tori using pseudo-differential operator theory. We establish that processes on $d$-dimensional tori require smoothness parameter $ν> 3d/2$ to achieve regularity $C^{(ν-3d/2)^-}_{\text{loc}}$, revealing a dimension-dependent threshold that contrasts with the Euclidean requirement of merely $ν> 0$. Our proof employs the Cardona-Martínez theory of pseudo-differential operators, providing new analytical tools to the study of random fields over manifolds. We also introduce the canonical-Matérn process, a parameter family that achieves regularity $C^{(ν-3d/2+2)^-}_{\text{loc}}$, gaining two orders of smoothness over standard Matérn processes.
