Stochastic PDEs and Dynamics

Stochastic PDEs and Dynamics

$186.99

Publication Date: 21st November 2016

This book explains mathematical theories of a collection of stochastic partial differential equations and their dynamical behaviors. Based on probability and stochastic process, the authors discuss... Read More
0 in stock
This book explains mathematical theories of a collection of stochastic partial differential equations and their dynamical behaviors. Based on probability and stochastic process, the authors discuss... Read More
Description

This book explains mathematical theories of a collection of stochastic partial differential equations and their dynamical behaviors. Based on probability and stochastic process, the authors discuss stochastic integrals, Ito formula and Ornstein-Uhlenbeck processes, and introduce theoretical framework for random attractors. With rigorous mathematical deduction, the book is an essential reference to mathematicians and physicists in nonlinear science.

Contents:
Preliminaries
The stochastic integral and Itô formula
OU processes and SDEs
Random attractors
Applications
Bibliography
Index

Details
  • Price: $186.99
  • Pages: 228
  • Publisher: De Gruyter
  • Imprint: De Gruyter
  • Publication Date: 21st November 2016
  • Illustration Note: 30 b/w ill., 10 b/w tbl.
  • ISBN: 9783110495102
  • Format: Hardcover
  • BISACs:
    MATHEMATICS / Differential Equations / Partial
    MATHEMATICS / Probability & Statistics / Stochastic Processes
    SCIENCE / Physics / Mathematical & Computational
    MATHEMATICS / Mathematical Analysis
    MATHEMATICS / Probability & Statistics / General
Author Bio

Boling Guo, Inst. of Applied Physics & Computational Maths;
Hongjun Gao, Nanjing Normal Univ.;
Xueke Pu, Chongqing Univ., China.

Table of Contents
Table of Content:
Chapter 1 Preliminaries
1.1 Preliminaries in probability
1.2 Preliminaries of stochastic process
1.3 Martingale
1.4 Wiener process and Brown motion
1.5 Poisson process
1.6 Levy process
1.7 The fractional Brownian motion
Chapter 2 The stochastic integral and Ito formula
2.1 Stochastic integral
2.2 Ito formula
2.3 The infnite dimensional case
2.4 Nuclear operator and Hilbert-Schmidt operator
Chapter 3 OU processes and SDEs
3.1 Ornstein-Uhlenbeck processes
3.2 Linear SDEs
3.3 Nonlinear SDEs
Chapter 4 Random attractors
4.1 Determinate nonautonomous systems
4.2 Stochastic dynamical systems
Chapter 5 Applications
5.1 Stochastic Ginzburg-Landau equation
5.2 Ergodicity for SGL with degenerate noise
5.3 Stochastic damped forced Ostrovsky equation
5.4 Simplifed quasi geostrophic model
5.5 Stochastic primitive equations
References

This book explains mathematical theories of a collection of stochastic partial differential equations and their dynamical behaviors. Based on probability and stochastic process, the authors discuss stochastic integrals, Ito formula and Ornstein-Uhlenbeck processes, and introduce theoretical framework for random attractors. With rigorous mathematical deduction, the book is an essential reference to mathematicians and physicists in nonlinear science.

Contents:
Preliminaries
The stochastic integral and Itô formula
OU processes and SDEs
Random attractors
Applications
Bibliography
Index

  • Price: $186.99
  • Pages: 228
  • Publisher: De Gruyter
  • Imprint: De Gruyter
  • Publication Date: 21st November 2016
  • Illustrations Note: 30 b/w ill., 10 b/w tbl.
  • ISBN: 9783110495102
  • Format: Hardcover
  • BISACs:
    MATHEMATICS / Differential Equations / Partial
    MATHEMATICS / Probability & Statistics / Stochastic Processes
    SCIENCE / Physics / Mathematical & Computational
    MATHEMATICS / Mathematical Analysis
    MATHEMATICS / Probability & Statistics / General

Boling Guo, Inst. of Applied Physics & Computational Maths;
Hongjun Gao, Nanjing Normal Univ.;
Xueke Pu, Chongqing Univ., China.

Table of Content:
Chapter 1 Preliminaries
1.1 Preliminaries in probability
1.2 Preliminaries of stochastic process
1.3 Martingale
1.4 Wiener process and Brown motion
1.5 Poisson process
1.6 Levy process
1.7 The fractional Brownian motion
Chapter 2 The stochastic integral and Ito formula
2.1 Stochastic integral
2.2 Ito formula
2.3 The infnite dimensional case
2.4 Nuclear operator and Hilbert-Schmidt operator
Chapter 3 OU processes and SDEs
3.1 Ornstein-Uhlenbeck processes
3.2 Linear SDEs
3.3 Nonlinear SDEs
Chapter 4 Random attractors
4.1 Determinate nonautonomous systems
4.2 Stochastic dynamical systems
Chapter 5 Applications
5.1 Stochastic Ginzburg-Landau equation
5.2 Ergodicity for SGL with degenerate noise
5.3 Stochastic damped forced Ostrovsky equation
5.4 Simplifed quasi geostrophic model
5.5 Stochastic primitive equations
References