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Three Classes of Nonlinear Stochastic Partial Differential Equations

Three Classes of Nonlinear Stochastic Partial Differential Equations

Three Classes of Nonlinear Stochastic Partial Differential Equations
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Three Classes of Nonlinear Stochastic Partial Differential Equations Hardback - 2013

by Jie Xiong

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New/New. Brand New Original US Edition, Perfect Condition. Printed in English. Excellent Quality, Service and customer satisfaction guaranteed!
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Details

  • Title Three Classes of Nonlinear Stochastic Partial Differential Equations
  • Author Jie Xiong
  • Binding Hardback
  • Condition New
  • Pages 176
  • Volumes 1
  • Language ENG
  • Publisher World Scientific Publishing Company
  • Publication date 2013-05-17
  • Features Bibliography, Index, Table of Contents
  • Bookseller's Inventory # BIBNNA-202313
  • ISBN 9789814452359 / 9814452351
  • Weight 0.97 lbs (0.44 kg)
  • Dimensions 9.1 x 6.1 x 0.7 in (23.11 x 15.49 x 1.78 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Mathematics
  • Dewey Decimal Code 519.2
  • Quantity available 2

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Reader reviews for Three Classes of Nonlinear Stochastic Partial Differential Equations

From the jacket flap

The study of measure-valued processes in random environments has seen some intensive research activities in recent years whereby interesting nonlinear stochastic partial differential equations (SPDEs) were derived. Due to the nonlinearity and the non-Lipschitz continuity of their coefficients, new techniques and concepts have recently been developed for the study of such SPDEs. These include the conditional Laplace transform technique, the conditional mild solution, and the bridge between SPDEs and some kind of backward stochastic differential equations. This volume provides an introduction to these topics with the aim of attracting more researchers into this exciting and young area of research. It can be considered as the first book of its kind. The tools introduced and developed for the study of measure-valued processes in random environments can be used in a much broader area of nonlinear SPDEs.
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