Galerkin-Chebyshev approximation of Gaussian random fields on compact Riemannian manifolds
Journal article, 2023

A new numerical approximation method for a class of Gaussian random fields on compact Riemannian manifolds is introduced. This class of random fields is characterized by the Laplace-Beltrami operator on the manifold. A Galerkin approximation is combined with a polynomial approximation using Chebyshev series. This so-called Galerkin-Chebyshev approximation scheme yields efficient and generic sampling algorithms for Gaussian random fields on manifolds. Strong and weak orders of convergence for the Galerkin approximation and strong convergence orders for the Galerkin-Chebyshev approximation are shown and confirmed through numerical experiments.

Compact Riemannian manifolds

Laplace-Beltrami operator

Gaussian random fields

Galerkin approximation

Whittle-Matérn fields

Chebyshev polynomials

Strong convergence

Weak convergence

Author

Annika Lang

University of Gothenburg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Mike Pereira

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

BIT Numerical Mathematics

0006-3835 (ISSN) 1572-9125 (eISSN)

Vol. 63 4 51

STOchastic Traffic NEtworks (STONE)

CHAIR, -- .

Chalmers, 2020-02-01 -- 2022-01-31.

Efficient approximation methods for random fields on manifolds

Swedish Research Council (VR) (2020-04170), 2021-01-01 -- 2024-12-31.

Stochastic Continuous-Depth Neural Networks

CHAIR, 2020-08-15 -- .

Subject Categories (SSIF 2011)

Computational Mathematics

Probability Theory and Statistics

Mathematical Analysis

Roots

Basic sciences

DOI

10.1007/s10543-023-00986-8

More information

Latest update

3/26/2026