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Multiscale and multilevel methods for porous media flow problemsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2015 (English)Licentiate thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Uppsala University, 2015.
##### Series

Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2015-003
##### National Category

Computational Mathematics
##### Research subject

Scientific Computing with specialization in Numerical Analysis
##### Identifiers

URN: urn:nbn:se:uu:diva-262276OAI: oai:DiVA.org:uu-262276DiVA, id: diva2:853228
#####

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Available from: 2015-09-09 Created: 2015-09-11 Last updated: 2017-08-31Bibliographically approved
##### List of papers

We consider two problems encountered in simulation of fluid flow through porous media. In macroscopic models based on Darcy's law, the permeability field appears as data.

The first problem is that the permeability field generally is not entirely known. We consider forward propagation of uncertainty from the permeability field to a quantity of interest. We focus on computing *p*-quantiles and failure probabilities of the quantity of interest. We propose and analyze improved standard and multilevel Monte Carlo methods that use computable error bounds for the quantity of interest. We show that substantial reductions in computational costs are possible by the proposed approaches.

The second problem is fine scale variations of the permeability field. The permeability often varies on a scale much smaller than that of the computational domain. For standard discretization methods, these fine scale variations need to be resolved by the mesh for the methods to yield accurate solutions. We analyze and prove convergence of a multiscale method based on the Raviart–Thomas finite element. In this approach, a low-dimensional multiscale space based on a coarse mesh is constructed from a set of independent fine scale patch problems. The low-dimensional space can be used to yield accurate solutions without resolving the fine scale.

1. Uncertainty quantification for approximate p-quantiles for physical models with stochastic inputs$(function(){PrimeFaces.cw("OverlayPanel","overlay785381",{id:"formSmash:j_idt480:0:j_idt484",widgetVar:"overlay785381",target:"formSmash:j_idt480:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. A multilevel Monte Carlo method for computing failure probabilities$(function(){PrimeFaces.cw("OverlayPanel","overlay853139",{id:"formSmash:j_idt480:1:j_idt484",widgetVar:"overlay853139",target:"formSmash:j_idt480:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Improved Monte Carlo methods for computing failure probabilities of porous media flow systems$(function(){PrimeFaces.cw("OverlayPanel","overlay849281",{id:"formSmash:j_idt480:2:j_idt484",widgetVar:"overlay849281",target:"formSmash:j_idt480:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Multiscale mixed finite elements$(function(){PrimeFaces.cw("OverlayPanel","overlay853134",{id:"formSmash:j_idt480:3:j_idt484",widgetVar:"overlay853134",target:"formSmash:j_idt480:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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