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:news270@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20180716T230659 DTSTART;TZID=Europe/Zurich:20131206T110000 SUMMARY:Seminar in Numerical Analysis: Mario S. Mommer (Universität Heidel berg) DESCRIPTION:Optimum experimental design (OED) is the problem of finding set ups for an experiment in such a way that the collected data allows for opt imally accurate estimation of the parameters of interest - taking into acc ount an experimental budget. In practice\, the parameters are only approxi mately known as a matter of course\, while at the same time\, solving an O ED problem is in a way equivalent to magnifying the dependence of the syst em response on these quantities.  As a consequence\, designs computed on the basis of a "good guess" of the parameters may underperform dramaticall y in practice\, especially for problems involving nonlinear models.\\r\\nI n this talk\, we consider robust formulations for optimum experimental des ign that work under significant uncertainty. Our focus is on problem setti ngs in which the model is described by differential equations of some type that are solved numerically. Our approach is based on a semi-infinite pro gramming formulation in which we exploit additional problem structure\, to gether with sparse grids\, to ensure tractability. The talk includes numer ical experiments to illustrate and compare the effectiveness of the approa ches. X-ALT-DESC:Optimum experimental design (OED) is the problem of finding setu ps for an experiment in such a way that the collected data allows for opti mally accurate estimation of the parameters of interest - taking into acco unt an experimental budget. In practice\, the parameters are only approxim ately known as a matter of course\, while at the same time\, solving an OE D problem is in a way equivalent to magnifying the dependence of the syste m response on these quantities. \; As a consequence\, designs computed on the basis of a "\;good guess"\; of the parameters may underper form dramatically in practice\, especially for problems involving nonlinea r models.\nIn this talk\, we consider robust formulations for optimum expe rimental design that work under significant uncertainty. Our focus is on p roblem settings in which the model is described by differential equations of some type that are solved numerically. Our approach is based on a semi- infinite programming formulation in which we exploit additional problem st ructure\, together with sparse grids\, to ensure tractability. The talk in cludes numerical experiments to illustrate and compare the effectiveness o f the approaches. DTEND;TZID=Europe/Zurich:20131206T120000 END:VEVENT END:VCALENDAR