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dc.contributor.author
Mazzonetto, Sara
dc.contributor.author
Salimova, Diyora
dc.date.accessioned
2020-06-10T12:11:22Z
dc.date.available
2020-01-31T03:10:40Z
dc.date.available
2020-02-11T09:33:26Z
dc.date.available
2020-06-10T12:11:22Z
dc.date.issued
2020
dc.identifier.issn
1532-9356
dc.identifier.issn
0736-2994
dc.identifier.other
10.1080/07362994.2019.1709503
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/396003
dc.identifier.doi
10.3929/ethz-b-000396003
dc.description.abstract
In this article, we propose an all-in-one statement which includes existence, uniqueness, regularity, and numerical approximations of mild solutions for a class of stochastic partial differential equations (SPDEs) with non-globally monotone nonlinearities. The proof of this result exploits the properties of an existing fully explicit space-time discrete approximation scheme, in particular the fact that it satisfies suitable a priori estimates. We also obtain almost sure and strong convergence of the approximation scheme to the mild solutions of the considered SPDEs. We conclude by applying the main result of the article to the stochastic Burgers equations with additive space-time white noise.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Taylor & Francis
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Stochastic Burgers equations
en_US
dc.subject
SPDEs
en_US
dc.subject
mild solution
en_US
dc.subject
existence
en_US
dc.subject
numerical approximation
en_US
dc.title
Existence, uniqueness, and numerical approximations for stochastic Burgers equations
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2020-01-20
ethz.journal.title
Stochastic Analysis and Applications
ethz.journal.volume
38
en_US
ethz.journal.issue
4
en_US
ethz.journal.abbreviated
Stoch. anal. appl.
ethz.pages.start
623
en_US
ethz.pages.end
646
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Higher order numerical approximation methods for stochastic partial differential equations
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02501 - Seminar für Angewandte Mathematik / Seminar for Applied Mathematics
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02501 - Seminar für Angewandte Mathematik / Seminar for Applied Mathematics::03951 - Jentzen, Arnulf (ehemalig) / Jentzen, Arnulf (former)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02501 - Seminar für Angewandte Mathematik / Seminar for Applied Mathematics
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02501 - Seminar für Angewandte Mathematik / Seminar for Applied Mathematics::03951 - Jentzen, Arnulf (ehemalig) / Jentzen, Arnulf (former)
ethz.grant.agreementno
175699
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte MINT
ethz.date.deposited
2020-01-31T03:10:43Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-06-10T12:11:33Z
ethz.rosetta.lastUpdated
2022-03-29T02:21:35Z
ethz.rosetta.versionExported
true
ethz.COinS
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