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UID:news930@dmi.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20191107T104152
DTSTART;TZID=Europe/Zurich:20191115T110000
SUMMARY:Seminar in Numerical Analysis: Michael Multerer (USI Lugano)
DESCRIPTION:The numerical simulation of physical phenomena is very well und
 erstood given that the input data are given exactly. However\, in practic
 e\, the collection of these data is usually subjected to measurement erro
 rs. The goal of uncertainty quantification is to assess those errors and 
 their possible impact on simulation results.In this talk\, we address diff
 erent numerical aspects of uncertainty quantification in elliptic partial
  differential equations on random domains. Starting from the modelling of
  random domains via random vector fields\, wediscuss how the corresponding
  Karhunen-Loève expansion can efficiently becomputed. For the discretisat
 ion of the partial differential equation\, we apply an adaptive Galerkin 
 framework. An a posteriori error estimator is presented\, which allows fo
 r the problem-dependent iterative refinement of all discretisation paramet
 ers and the assessment of the achieved error reduction. The proposed appr
 oach is demonstrated in numerical benchmark problems.\\r\\nFor further in
 formation about the seminar\, please visit this webpage [t3://page?uid=11
 15].
X-ALT-DESC:<p>The numerical simulation of physical phenomena is very well u
 nderstood given that&nbsp\;the input data are given exactly. However\, in 
 practice\, the collection of these&nbsp\;data is usually subjected to meas
 urement errors. The goal of uncertainty quantification&nbsp\;is to assess 
 those errors and their possible impact on simulation results.In this talk\
 , we address different numerical aspects of uncertainty quantification&nbs
 p\;in elliptic partial differential equations on random domains.&nbsp\;Sta
 rting from the modelling of random domains via random vector fields\, wedi
 scuss how the corresponding Karhunen-Loève expansion can efficiently beco
 mputed. For the discretisation of the partial differential equation\,&nbsp
 \;we apply an adaptive Galerkin framework. An a posteriori error estimator
  is presented\,&nbsp\;which allows for the problem-dependent iterative ref
 inement of all discretisation parameters&nbsp\;and the assessment of the a
 chieved error reduction. The proposed approach is demonstrated&nbsp\;in nu
 merical benchmark problems.</p>\n<p>For further information about the semi
 nar\, please visit this&nbsp\;<a href="t3://page?uid=1115" title="Opens in
 ternal link in current window" class="internal-link">webpage</a>.</p>
DTEND;TZID=Europe/Zurich:20191115T120000
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