Stochastic Numerics for the Boltzmann Equation [electronic resource] /by Sergej Rjasanow, Wolfgang Wagner.
by Rjasanow, Sergej [author.]; Wagner, Wolfgang [author.]; SpringerLink (Online service).
Material type:
BookSeries: Springer Series in Computational Mathematics: 37Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.Description: XIII, 256 p. 98 illus. online resource.ISBN: 9783540276890.Subject(s): Mathematics | Differential equations, partial | Numerical analysis | Distribution (Probability theory) | Mathematical physics | Mathematics | Numerical Analysis | Probability Theory and Stochastic Processes | Mathematical and Computational Physics | Partial Differential EquationsDDC classification: 518 Online resources: Click here to access online
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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.
Kinetic theory -- Related Markov processes -- Stochastic weighted particle method -- Numerical experiments.
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| MAIN LIBRARY | QA297-299.4 (Browse shelf) | Available |
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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