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Multiscale methods for problems with complex geometry
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
2017 (English)In: Computer Methods in Applied Mechanics and Engineering, ISSN 0045-7825, E-ISSN 1879-2138, Vol. 321, 103-123 p.Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2017. Vol. 321, 103-123 p.
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:uu:diva-262266DOI: 10.1016/j.cma.2017.03.023OAI: oai:DiVA.org:uu-262266DiVA: diva2:853151
Available from: 2017-03-31 Created: 2015-09-11 Last updated: 2017-05-10Bibliographically approved
In thesis
1. Multiscale Methods and Uncertainty Quantification
Open this publication in new window or tab >>Multiscale Methods and Uncertainty Quantification
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis we consider two great challenges in computer simulations of partial differential equations: multiscale data, varying over multiple scales in space and time, and data uncertainty, due to lack of or inexact measurements.

We develop a multiscale method based on a coarse scale correction, using localized fine scale computations. We prove that the error in the solution produced by the multiscale method decays independently of the fine scale variation in the data or the computational domain. We consider the following aspects of multiscale methods: continuous and discontinuous underlying numerical methods, adaptivity, convection-diffusion problems, Petrov-Galerkin formulation, and complex geometries.

For uncertainty quantification problems we consider the estimation of p-quantiles and failure probability. We use spatial a posteriori error estimates to develop and improve variance reduction techniques for Monte Carlo methods. We improve standard Monte Carlo methods for computing p-quantiles and multilevel Monte Carlo methods for computing failure probability.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. 32 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1287
Keyword
multiscale methods, finite element method, discontinuous Galerkin, Petrov-Galerkin, a priori, a posteriori, complex geometry, uncertainty quantification, multilevel Monte Carlo, failure probability
National Category
Computational Mathematics
Research subject
Scientific Computing with specialization in Numerical Analysis
Identifiers
urn:nbn:se:uu:diva-262354 (URN)978-91-554-9336-3 (ISBN)
Public defence
2015-10-30, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2015-10-08 Created: 2015-09-14 Last updated: 2015-10-12Bibliographically approved

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Elfverson, DanielMålqvist, Axel

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