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SUMMARY:GOAL-ORIENTED ERROR ESTIMATION FOR PARAMETER-DEPENDENT NONLINEAR P
 ROBLEMS\, APPLICATION TO SENSITIVITY ANALYSIS - Clémentine Prieur (Univer
 sité de Grenoble\; INRIA Grenoble - Rhône-Alpes)
DTSTART:20180620T093000Z
DTEND:20180620T120000Z
UID:TALK107308@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:During this talk\, we will present a numerically efficient met
 hod to bound the error that is made when approximating the output of a non
 linear problem depending on an unknown parameter (described by a probabili
 ty distribution). The class of nonlinear problems under consideration incl
 udes high-dimensional nonlinear problems with a nonlinear output function.
  A goal-oriented probabilistic bound is computed by considering two phases
 . An offline phase dedicated to the computation of a reduced model during 
 which the full nonlinear problem needs to be solved only a small number of
  times. The second phase is an online phase which approximates the output.
  This approach is applied to a toy model and to a nonlinear partial differ
 ential equation\, more precisely the Burgers equation with unknown initial
  condition given by two probabilistic parameters. The savings in computati
 onal cost are evaluated and presented.
LOCATION:Seminar Room 2\, Newton Institute
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