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A Priori Feedback E...
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Lind, Martin,1985-Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
(author)
A Priori Feedback Estimates for Multiscale Reaction-Diffusion Systems
- Article/chapterEnglish2018
Publisher, publication year, extent ...
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2017-09-28
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Taylor & Francis,2018
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Numbers
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LIBRIS-ID:oai:DiVA.org:kau-62808
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https://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-62808URI
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https://doi.org/10.1080/01630563.2017.1369996DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
Notes
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We study the approximation of a multiscale reaction–diusion system posed on both macroscopic and microscopic space scales. The coupling between the scales is done through micro– macro ux conditions. Our target system has a typical structure for reaction–diusion ow problems in media with distributed microstructures (also called, double porosity materials). Besides ensuring basic estimates for the convergence of two-scale semidiscrete Galerkin approximations, we provide a set of a priori feedback estimates and a local feedback error estimator that help in designing a distributed-high-errors strategy to allow for a computationally ecient zooming in and out from microscopic structures. The error control on the feedback estimates relies on two-scale-energy, regularity, and interpolation estimates as well as on a ne bookeeping of the sources responsible with the propagation of the (multiscale) approximation errors. The working technique based on a priori feedback estimates is in principle applicable to a large class of systems of PDEs with dual structure admitting strong solutions. A
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Added entries (persons, corporate bodies, meetings, titles ...)
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Muntean, Adrian,1974-Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)(Swepub:kau)adrimunt
(author)
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Karlstads universitetInstitutionen för matematik och datavetenskap (from 2013)
(creator_code:org_t)
Related titles
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In:Numerical Functional Analysis and Optimization: Taylor & Francis39:4, s. 413-4370163-05631532-2467
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