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Springer Stochastic Numerics for Mathematical Physics 2nd Ed

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Description

Stay updated with the latest advancements in scientific computation with this enlarged and revised second edition of Stochastic Numerics for Mathematical Physics. Published by Springer, this updated text reflects major developments in the field since the 2004 release. It covers essential new topics including mean-square and weak approximations for Stochastic Differential Equations (SDEs) with nonglobally Lipschitz coefficients. Readers will find detailed information on the concept of rejecting trajectories, conditional probabilistic representations, and practical variance reduction using regression methods. The book also explores the multi-level Monte Carlo method, computing ergodic limits, and various classes of geometric integrators. This edition is an essential resource for professionals and students focused on the evolution of stochastic numerics within mathematical physics.

Key Features

This enlarged and revised 2nd edition from Springer includes major developments in stochastic numerics since 2004.

Covers new topics such as mean-square and weak approximations for SDEs with nonglobally Lipschitz coefficients.

Includes specialized content on the concept of rejecting trajectories and conditional probabilistic representations.

Provides technical guidance on practical variance reduction using regression methods.

Detailed sections available on the multi-level Monte Carlo method and computing ergodic limits.

Explores additional classes of geometric integrators for advanced scientific computation.

Product Specifications

Colour
Sd-mo28
Size
7' X 8'
Format
hardcover
Domain
Amazon UK
Release Date
04 December 2021
Listed Since
19 August 2020

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