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:news276@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20180716T231855 DTSTART;TZID=Europe/Zurich:20130503T110000 SUMMARY:Seminar in Numerical Analysis: Rüdiger Schultz (Universität Duisb urg-Essen) DESCRIPTION:This talk aims at demonstrating how concepts and techniques whi ch are well-established in operations research may serve as blueprints fo r approaching shape optimization with linearized  elasticity and stocha stic loading. Stochastic shape optimization problems are considered from a two-stage viewpoint: In a first stage\, without anticipation of the ran dom loading\, the shape has to be fixed. After realization  of the load\ , the displacement obtained from solving the elasticity boundary value pr oblem then may be seen as a second-stage (or recourse) action\, and the v ariational problem of the weak formulation as a second-stage optimization problem.\\r\\nAt this point\, there is a perfect match with two-stage st ochastic programming: after having taken a non-anticipative decision in  the first stage\, and having observed the random data\, a well-defined second-stage problem remains and is solved to optimality. Suitable object ive functions complete the formal descriptions of the models\, for instan ce\, costs in the stochastic-programming setting and compliance or tracki ng functionals in shape optimization.\\r\\nStochastic programming now off ers a wide collection of models to address shape optimization under uncer tainty. This starts with risk neutral models\, is continued by mean-risk  optimization involving different risk measures\, and will finally lead to analogues in shape optimization of decision problems with stochastic- order (or dominance) constraints.\\r\\nIn the talk we will present these m odels\, discuss solution methods\, and report some computational tests. X-ALT-DESC:This talk aims at demonstrating how concepts and techniques whic h are well-established in operations research may serve as blueprints for approaching shape optimization with linearized  \;elasticity and st ochastic loading. Stochastic shape optimization problems are considered f rom a two-stage viewpoint: In a first stage\, without anticipation of the random loading\, the shape has to be fixed. After realization  \;of the load\, the displacement obtained from solving the elasticity boundary value problem then may be seen as a second-stage (or recourse) action\, and the variational problem of the weak formulation as a second-stage opt imization problem.\nAt this point\, there is a perfect match with two-sta ge stochastic programming: after having taken a non-anticipative decision in  \;the first stage\, and having observed the random data\, a well -defined second-stage problem remains and is solved to optimality. Suitab le objective functions complete the formal descriptions of the models\, f or instance\, costs in the stochastic-programming setting and compliance or tracking functionals in shape optimization.\nStochastic programming no w offers a wide collection of models to address shape optimization under uncertainty. This starts with risk neutral models\, is continued by mean- risk  \;optimization involving different risk measures\, and will fin ally lead to analogues in shape optimization of decision problems with s tochastic-order (or dominance) constraints.\nIn the talk we will present t hese models\, discuss solution methods\, and report some computational tes ts. DTEND;TZID=Europe/Zurich:20130503T120000 END:VEVENT END:VCALENDAR