Harden, C: Real Time Computing with the Parareal Algorithm Solving time-dependent differential equations with increased speed
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- Englisch ausgewählt
Fr. 76.90
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
01.02.2009
Verlag
VDMSeitenzahl
92
Maße (L/B/H)
22/15/0.6 cm
Gewicht
153 g
Sprache
Englisch
ISBN
978-3-639-13085-0
This work presents and evaluates a particular
algorithm used for the
real time computations of time dependent ordinary and
partial
differential equations which employs a
parallelization strategy over
the temporal domain. We also discuss the coupling of
this method
with another popular technique used for real time
computations,
model reduction, which will be shown to provide more
gains than
either method alone. In particular, we look at
reduced order
modeling based on proper orthogonal decompositions.
We present some applications in terms of solving
time dependent
nonlinear partial differential equations and solving
these equations
with a coupled approach of combining model reduction
and the
parareal algorithm . The performance of this method,
both
numerically and computationally, is discussed in
terms of the gains
in speedup and efficiency, and in terms of the
scalability of the
parallelization of the temporal domain on a larger
and larger
set of compute nodes or processors.
algorithm used for the
real time computations of time dependent ordinary and
partial
differential equations which employs a
parallelization strategy over
the temporal domain. We also discuss the coupling of
this method
with another popular technique used for real time
computations,
model reduction, which will be shown to provide more
gains than
either method alone. In particular, we look at
reduced order
modeling based on proper orthogonal decompositions.
We present some applications in terms of solving
time dependent
nonlinear partial differential equations and solving
these equations
with a coupled approach of combining model reduction
and the
parareal algorithm . The performance of this method,
both
numerically and computationally, is discussed in
terms of the gains
in speedup and efficiency, and in terms of the
scalability of the
parallelization of the temporal domain on a larger
and larger
set of compute nodes or processors.
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