Stochastic Stability of Differential Equations in Abstract Spaces

by Kai Liu

Overview

The stability of stochastic differential equations in abstract, mainly Hilbert, spaces receives a unified treatment in this self-contained book. It covers basic theory as well as computational techniques for handling the stochastic stability of systems from mathematical, physical and biological problems. Its core material is divided into three parts devoted respectively to the stochastic stability of linear systems, non-linear systems, and time-delay systems. The focus is on stability of stochastic dynamical processes affected by white noise, which are described by partial differential equations such as the Navier–Stokes equations. A range of mathematicians and scientists, including those involved in numerical computation, will find this book useful. It is also ideal for engineers working on stochastic systems and their control, and researchers in mathematical physics or biology.

Product Details

ISBN-13:	9781108705172
Publisher:	Cambridge University Press
Publication date:	05/02/2019
Series:	London Mathematical Society Lecture Note Series , #453
Pages:	276
Product dimensions:	5.98(w) x 8.98(h) x 0.63(d)

About the Author

Kai Liu is a mathematician at the University of Liverpool. His research interests include stochastic analysis, both deterministic and stochastic partial differential equations, and stochastic control. His recent research activities focus on stochastic functional differential equations in abstract spaces. He is a member of the editorial boards of several international journals including the Journal of Stochastic Analysis and Applications and Statistics and Probability Letters.

Table of Contents

Preface; 1. Preliminaries; 2. Stability of linear stochastic differential equations; 3. Stability of non linear stochastic differential equations; 4. Stability of stochastic functional differential equations; 5. Some applications related to stochastic stability; Appendix; References; Index.

Editorial Reviews

‘The text itself is rather detailed, and therefore can be understood by graduate students and young researchers who have taken a solid course in stochastic analysis. Many examples are provided throughout the text to explain the finer points in the results.’ Mar´ıa J. Garrido-Atienza, MathSciNet

From the Publisher