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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Uncertainty Quantification with Multi-Level and Mu
 lti-Index methods - Raul Fidel Tempone (King Abdul
 lah University of Science and Technology (KAUST))
DTSTART;TZID=Europe/London:20180208T143000
DTEND;TZID=Europe/London:20180208T153000
UID:TALK100219AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/100219
DESCRIPTION:We start by recalling the Monte Carlo and Multi-le
 vel Monte Carlo (MLMC) methods for computing stati
 stics of the solution of a Partial Differential Eq
 uation with random data. Then\, we present the Mul
 ti-Index Monte Carlo (MIMC) and Multi-Index Stocha
 stic Collocation  (MISC) methods. MIMC is both a s
 tochastic version of the combination technique int
 roduced by Zenger\, Griebel and collaborators and 
 an extension of the MLMC method first described by
  Heinrich and Giles. Instead of using first-order 
 differences as in MLMC\, MIMC uses mixed differenc
 es to reduce the variance of the hierarchical diff
 erences dramatically\, thus yielding improved conv
 ergence rates.  MISC is a deterministic combinatio
 n technique that also uses mixed differences to ac
 hieve better complexity than MIMC\, provided enoug
 h regularity. During the presentation\, we will sh
 owcase the behavior of the numerical methods in ap
 plications\, some of them arising in the context o
 f Regression based Surrogates and Optimal Experime
 ntal Design.  Coauthors: J. Beck\, L. Espath (KAUS
 T)\, A.-L. Haji-Ali (Oxford)\, Q. Long (UT)\, F. N
 obile (EPFL)\,  M. Scavino (UdelaR)\, L. Tamellini
  (IMATI)\, S. Wolfers (KAUST)   Webpages:  https:/
 /stochastic_numerics.kaust.edu.sa https://sri-uq.k
 aust.edu.sa
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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