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

By: Contributor(s): Material type: TextTextSeries: 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 title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 518 23
LOC classification:
  • QA297-299.4
Online resources:
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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
E-BOOKS ISI Library, Kolkata Not for loan EB941
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.

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