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CATEGORIES:Engineering Department Bio- and Micromechanics Sem
inars
SUMMARY:Numerical solution of partial differential equatio
ns with random coefficients: a stochastic finite e
lement approach - Dr Eveline Rosseel\, Katholieke
Universiteit Leuven
DTSTART;TZID=Europe/London:20101126T140000
DTEND;TZID=Europe/London:20101126T150000
UID:TALK25981AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/25981
DESCRIPTION:Mathematical models of engineering systems and phy
sical processes typically take the form of a parti
al differential equation (PDE). Variability or unc
ertainty on coefficients of a PDE can be expressed
by introducing random variables\, random fields o
r random processes into the PDE. Recently develope
d stochastic finite element methods enable the con
struction of high-order accurate solutions of a st
ochastic PDE\, while reducing the high computation
al cost of more standard uncertainty quantificatio
n methods\, such as the Monte Carlo simulation met
hod.\n\n\nIn this talk\, we outline the methodolog
y of the two main variants of the stochastic finit
e element method\, i.e.\, the stochastic collocati
on and the stochastic Galerkin method. Both method
s transform a stochastic PDE into a system of dete
rministic PDEs. In the latter case\, the number of
deterministic PDEs is generally smaller than for
the stochastic collocation method\, but the system
is more complicated to solve. We will point out h
ow these methods can be applied to efficiently sol
ve a small flow problem through random porous medi
a. Multigrid techniques lead to a fast solution of
the resulting systems. These simulations can be e
xtended to solve groundwater flow problems or to p
erform oil reservoir simulations.
LOCATION:Oatley Seminar Room\, Department of Engineering
CONTACT:Ms Helen Gardner
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