BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Sabre//Sabre VObject 4.5.8//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Europe/Zurich
X-LIC-LOCATION:Europe/Zurich
TZURL:http://tzurl.org/zoneinfo/Europe/Zurich
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19810329T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19961027T030000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:news224@dmi.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20180716T181506
DTSTART;TZID=Europe/Zurich:20171110T110000
SUMMARY:Seminar in Numerical Analysis: Martin Eigel (WIAS Berlin)
DESCRIPTION:The Stochastic Galerkin FEM (SGFEM) is a common method to numer
 ically  solve PDEs with random data with the aim to obtain a functional  r
 epresentation of the stochastic solution. As with any spectral method\,  t
 he curse of dimensionality renders the approach very challenging  whenever
  the randomness depends on a large or even infinite set of  parameters. Th
 is makes function space adaptation and model reduction  strategies a neces
 sity. We review adaptive SGFEM based on reliable a  posteriori error estim
 ators for the affine and the lognormal cases. As  an alternative to a spar
 se discretisation\, the representation in a  hierarchical tensor format is
  examined. Moreover\, as an application of  the result\, we present an ada
 ptive method for explicit sampling-free  Bayesian inversion.
X-ALT-DESC:The Stochastic Galerkin FEM (SGFEM) is a common method to numeri
 cally  solve PDEs with random data with the aim to obtain a functional  re
 presentation of the stochastic solution. As with any spectral method\,  th
 e curse of dimensionality renders the approach very challenging  whenever 
 the randomness depends on a large or even infinite set of  parameters. Thi
 s makes function space adaptation and model reduction  strategies a necess
 ity. We review adaptive SGFEM based on reliable a  posteriori error estima
 tors for the affine and the lognormal cases. As  an alternative to a spars
 e discretisation\, the representation in a  hierarchical tensor format is 
 examined. Moreover\, as an application of  the result\, we present an adap
 tive method for explicit sampling-free  Bayesian inversion.
DTEND;TZID=Europe/Zurich:20171110T120000
END:VEVENT
END:VCALENDAR