<div class="csl-bib-body">
<div class="csl-entry">Gerencser, M. (2023, June 12). <i>Beyond strong rate 1/2 for approximations of space-time white noise driven SPDEs</i> [Conference Presentation]. Foundations of Computational Mathematics 2023, Paris, France.</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192322
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dc.description.abstract
We consider 1+1-dimensional space-time white noise driven reaction-diffusion type equations (e.g. stochastic Allen-Cahn equation), more precisely their full discretisation. Strong rate of convergence 1/2 has been proven before and has been considered optimal, supported by rigorous lower bounds. We show that weakening the path topology where the error is measured results in higher strong rate of convergence (which is not the case for finite dimensional SDEs). The proof leverages tools from singular SPDEs.
en
dc.language.iso
en
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dc.subject
Stochastic PDEs
en
dc.subject
finite differences
en
dc.title
Beyond strong rate 1/2 for approximations of space-time white noise driven SPDEs
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.type.category
Conference Presentation
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tuw.publication.invited
invited
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tuw.researchTopic.id
A3
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tuw.researchTopic.name
Fundamental Mathematics Research
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E101-01 - Forschungsbereich Analysis
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tuw.event.name
Foundations of Computational Mathematics 2023
en
tuw.event.startdate
12-06-2023
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tuw.event.enddate
21-06-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Paris
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tuw.event.country
FR
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tuw.event.institution
Sorbonne Université
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tuw.event.presenter
Gerencser, Mate
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
100
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item.grantfulltext
none
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item.openairetype
conference paper not in proceedings
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item.fulltext
no Fulltext
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item.languageiso639-1
en
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item.openairecristype
http://purl.org/coar/resource_type/c_18cp
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item.cerifentitytype
Publications
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crisitem.author.dept
E101-01 - Forschungsbereich Analysis
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crisitem.author.parentorg
E101 - Institut für Analysis und Scientific Computing