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  3. Stochastic Stability of Differential Equations in Abstract Spaces
  • Cited by 5
      • Kai Liu, Tianjin Normal University, China
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    • Publisher:
      Cambridge University Press
      Publication date:
      April 2019
      May 2019
      ISBN:
      9781108653039
      9781108705172
      DOI:
      https://doi.org/10.1017/9781108653039
      Dimensions:
      Weight & Pages:
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.42kg, 276 Pages
      • Subjects:
      Mathematics (general), Mathematics, Differential and Integral Equations, Dynamical Systems and Control Theory, Probability Theory and Stochastic Processes, Statistics and Probability
      Series:
      London Mathematical Society Lecture Note Series (453)
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    • Selected: Digital
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    Subjects:
    Mathematics (general), Mathematics, Differential and Integral Equations, Dynamical Systems and Control Theory, Probability Theory and Stochastic Processes, Statistics and Probability
    Series:
    London Mathematical Society Lecture Note Series (453)
    Information

    Book description

    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.

    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 Source: MathSciNet

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    Contents

    Contents
    • 1 - Preliminaries
      pp 1-45
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