[2406.19799] Spectral approximation of a new class of stochastic fractional evolution equations Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Mathematics > Numerical Analysis arXiv:2406.19799 (math) [Submitted on 28 Jun 2024 ( v1 ), last revised 29 Apr 2026 (this version, v4)] Title: Spectral approximation of a new class of stochastic fractional evolution equations Authors: S. Knutsen Furset View a PDF of the paper titled Spectral approximation of a new class of stochastic fractional evolution equations, by S. Knutsen Furset View PDF HTML (experimental) Abstract: A method for numerical approximation of a new class of fractional parabolic stochastic evolution equations is introduced and analysed. This class of equations has recently been proposed as a space-time extension of the SPDE-method in spatial statistics. A truncation of the spectral basis function expansion is used to discretise in space, and then a quadrature is used to approximate the temporal evolution of each basis coefficient. Strong error bounds are proved both for the spectral and temporal approximations. The method is tested and the results are verified by several numerical experiments. Subjects: Numerical Analysis (math.NA) MSC classes: 60H15, 65C30 Cite as: arXiv:2406.19799 [math.NA] (or arXiv:2406.19799v4 [math.NA] for this version) https://doi.org/10.48550/arXiv.2406.19799 Focus to learn more arXiv-issued DOI via DataCite Journal reference: S. K. Furset. Spectral approximation of a class of stochastic time-fractional evolution equations. Advances in Computational Mathematics, 52(2), 2026 Related DOI : https://doi.org/10.1007/s10444-026-10291-x Focus to learn more DOI(s) linking to related resources Submission history From: S. Knutsen Furset [ view email ] [v1] Fri, 28 Jun 2024 10:12:38 UTC (959 KB) [v2] Wed, 25 Jun 2025 12:49:59 UTC (2,390 KB) [v3] Tue, 28 Apr 2026 07:55:06 UTC (2,378 KB) [v4] Wed, 29 Apr 2026 12:10:02 UTC (2,378 KB) Full-text links: Access Paper: View a PDF of the paper titled Spectral approximation of a new class of stochastic fractional evolution equations, by S. Knutsen Furset View PDF HTML (experimental) TeX Source view license Current browse context: math.NA < prev | next > new | recent | 2024-06 Change to browse by: cs cs.NA math References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from