Non-stationary Gaussian random fields on hypersurfaces: Sampling and strong error analysis
Preprint, 2024

A flexible model for non-stationary Gaussian random fields on hypersurfaces is this http URL class of random fields on curves and surfaces is characterized by an amplitude spectral density of a second order elliptic differential this http URL is done by a Galerkin-Chebyshev approximation based on the surface finite element method and Chebyshev polynomials. Strong error bounds are shown with convergence rates depending on the smoothness of the approximated random field. Numerical experiments that confirm the convergence rates are presented.

Gaussian random fields

Chebyshev approximation

Gaussian processes

stochastic partial differential equations

non-stationary random fields

surface finite element method

Author

Erik Jansson

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Annika Lang

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Mike Pereira

Chalmers, Electrical Engineering, Systems and control

Efficient approximation methods for random fields on manifolds

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

Time-Evolving Stochastic Manifolds (StochMan)

European Commission (EC) (EC/HE/101088589), 2023-09-01 -- 2028-08-31.

Subject Categories

Mathematics

Computational Mathematics

Geometry

Probability Theory and Statistics

Mathematical Analysis

DOI

10.48550/arXiv.2406.08185

More information

Latest update

12/20/2024