Stochastic Numerics for the Boltzmann Equation [electronic resource] / by Sergej Rjasanow, Wolfgang Wagner.

By:

Rjasanow, Sergej [author.]

Contributor(s):

Material type: Text

TextSeries: Springer Series in Computational Mathematics ; 37Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Description: XIV, 256 p. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783540276890

Subject(s):

Additional physical formats: Printed edition:: No titleDDC classification:

518 23

LOC classification:

QA297-299.4

Online resources:

Click here to access online

Contents:

Kinetic theory -- Related Markov processes -- Stochastic weighted particle method -- Numerical experiments.

In: Springer eBooksSummary: Stochastic numerical methods play an important role in large scale computations in the applied sciences. The first goal of this book is to give a mathematical description of classical direct simulation Monte Carlo (DSMC) procedures for rarefied gases, using the theory of Markov processes as a unifying framework. The second goal is a systematic treatment of an extension of DSMC, called stochastic weighted particle method. This method includes several new features, which are introduced for the purpose of variance reduction (rare event simulation). Rigorous convergence results as well as detailed numerical studies are presented.

Holdings ( 1 )
Title notes ( 2 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
eBook	e-Library	EBook		Available

Total holds: 0

Kinetic theory -- Related Markov processes -- Stochastic weighted particle method -- Numerical experiments.

Stochastic numerical methods play an important role in large scale computations in the applied sciences. The first goal of this book is to give a mathematical description of classical direct simulation Monte Carlo (DSMC) procedures for rarefied gases, using the theory of Markov processes as a unifying framework. The second goal is a systematic treatment of an extension of DSMC, called stochastic weighted particle method. This method includes several new features, which are introduced for the purpose of variance reduction (rare event simulation). Rigorous convergence results as well as detailed numerical studies are presented.