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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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