{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/non-stationary-gaussian-random-fields-on","title":"Non-stationary Gaussian random fields on hypersurfaces: Sampling and strong error analysis","arxiv_id":"2406.08185","date":"2024-06-12","proceeding":null,"authors":["Erik Jansson","Annika Lang","Mike Pereira"],"abstract":"A flexible model for non-stationary Gaussian random fields on hypersurfaces is introduced. The class of random fields on curves and surfaces is characterized by an amplitude spectral density of a second order elliptic differential operator. Sampling 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.","url_abs":"https://arxiv.org/abs/2406.08185v2","url_pdf":"https://arxiv.org/pdf/2406.08185v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"non-stationary-gaussian-random-fields-on","repo_url":"https://github.com/mike-pereira/SFEMsim","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}