Stochastic Cauchy Problems in Infinite Dimensions

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A01=Irina V. Melnikova
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Author_Irina V. Melnikova
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Banach Space
Category1=Non-Fiction
Category=PBKJ
Category=PBWL
Cauchy Problem
Closed Linear Operator
Colombeau Algebra
COP=United States
Cylindrical Wiener Process
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distribution solutions
eq_isMigrated=2
eq_nobargain
Exponentially Bounded
Feynman Kac Theorem
Gelfand Shilov Spaces
Generalized Random Variables
Hilbert Schmidt Operators
Hilbert Space
Hilbert space methods
Hilbert spaces
infinite-dimensional analysis
infinite-dimensional stochastic analysis
Infinitely Differentiable
integral equations
Integrated Semigroups
Language_English
Laplace Transform
Mild Solution
Ordinary Differential Equations
PA=Available
Price_€100 and above
PS=Active
regularization
regularized semi-groups
semi-group and distribution methods
Semi-group Property
semi-group theory
Separable Hilbert Spaces
softlaunch
Space S?
Space Sα
Stochastic Convolution
stochastic differential equations
stochastic evolution equations in Hilbert spaces
Stochastic Integrals
stochastic partial differential equations
Strongly Continuous
Trace Class Operator
Weak Solution
white noise analysis
white noise calculus
Wiener Process

Product details

  • ISBN 9781482210507
  • Weight: 576g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Feb 2016
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Stochastic Cauchy Problems in Infinite Dimensions: Generalized and Regularized Solutions presents stochastic differential equations for random processes with values in Hilbert spaces. Accessible to non-specialists, the book explores how modern semi-group and distribution methods relate to the methods of infinite-dimensional stochastic analysis. It also shows how the idea of regularization in a broad sense pervades all these methods and is useful for numerical realization and applications of the theory.

The book presents generalized solutions to the Cauchy problem in its initial form with white noise processes in spaces of distributions. It also covers the "classical" approach to stochastic problems involving the solution of corresponding integral equations. The first part of the text gives a self-contained introduction to modern semi-group and abstract distribution methods for solving the homogeneous (deterministic) Cauchy problem. In the second part, the author solves stochastic problems using semi-group and distribution methods as well as the methods of infinite-dimensional stochastic analysis.

Irina V. Melnikova is a professor in the Institute of Mathematics and Computer Sciences at Ural Federal University. Her research interests include analysis, applied mathematics, and probability theory.

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