Stochastic Numerics for the Boltzmann Equation / by Sergej Rjasanow, Wolfgang Wagner.

Por:

Rjasanow, Sergej [autor]

Colaborador(es):

Tipo de material: Texto

TextoSeries Springer Series in Computational Mathematics ; 37Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Descripción: xiii, 256 páginas 98 ilustraciones recurso en líneaTipo de contenido:

texto

Tipo de medio:

computadora

Tipo de portador:

recurso en línea

ISBN:

9783540276890

Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:

QA297-299.4

Recursos en línea:

Conectar a Springer E-Books (Para consulta externa se requiere previa autentificaciÃ³n en Biblioteca Digital UANL)

Contenidos:

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

Resumen: 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.

Existencias ( 0 )
Notas de título ( 4 )

No hay ítems correspondientes a este registro

Springer eBooks

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.

Para consulta fuera de la UANL se requiere clave de acceso remoto.