An Introduction to Stochastic Dynamics-(随机动力系统导论)-第42号 本书特色

随机动力系统是一个入门较难的新兴领域。段金 桥、杨乐编著的《随机动力系统导论(英文版)(精)/ 纯粹数学与应用数学专著》是这个领域的一个较为通 俗易懂的引论。在本书的**部分，作者从简单的随 机动力系统实际例子出发，引导读者回顾概率论和白 噪声的基本知识，深入浅出地介绍随机微积分，然后 自然地展开随机微分方程的讨论。

An Introduction to Stochastic Dynamics-(随机动力系统导论)-第42号 目录

chapter 1 introduction
　1.1 examples of deterministic dynamical systems
　1.2 examples of stochastic dynamical systems
　1.3 mathematical modeling with stochastic differential equations
　1.4 outline of this book
　1.5 problems
chapter 2 background in analysis and probability
　2.1 euclidean space
　2.2 hilbert, banach and metric spaces
　2.3 taylor expansions
　2.4 improper integrals and cauchy principal values
　2.5 some useful inequalities
　　2.5.1 young's inequality
　　2.5.2 cronwall inequality
　　2.5.3 cauchy-schwaxz inequality
　　2.5.4 hslder inequality
　　2.5.5 minkowski inequality
　2.6 hslder spaces, sobolev spaces and related inequalities
　2.7 probability spaces
　　2.7.1 scalar random variables
　　2.7.2 random vectors
　　2.7.3 gaussian random variables
　　2.7.4 non-gaussian random variables
　2.8 stochastic processes
　2.9 coovergence concepts
　2.10 simulation
　2.11 problems
chapter 3 noise
　3.1 brownian motion
　　3.1.1 brownian motion in r1
　　3.1.2 brownian motion in rn~
　3.2 what is gaussian white noise
　3.3* a mathematical model for gaussian white noise
　　3.3.1 generalized derivatives
　　3.3.2 gaussian white noise
　3.4 simulation
　3.5 problems
chapter 4 a crash course in stochastic differential equations
　4.1 differential equations with noise
　4.2 riemann-stieltjes integration
　4.3 stochastic integration and stochastic differential equations
　　4.3.1 motivation
　　4.3.2 definition of it5 integral
　　4.3.3 practical calculations
　　4.3.4 stratonovich integral
　　4.3.5 examples
　　4.3.6 properties of it6 integrals
　　4.3.7 stochastic differential equations
　　4.3.8 sdes in engineering and science literature
　　4.3.9 sdes with two-sided brownian motions
　4.4 it6's formula
　　4.4.1 motivation for stochasticchain rules
　　4.4.2 its's formula in scalar case
　　4.4.3 it6's formula in vector case
　　4.4.4 stochastic product rule and integration by parts
　4.5 linear stochastic differential equations
　4.6 nonlinear stochastic differential equations
　　4.6.1 existence, uniqueness and smoothness
　　4.6.2 probability measure px and expectation ex associated with an sde
　4.7 conversion between it5 and stratonovich stochastic differential
　　equations
　　4.7.1 scalar sdes
　　4.7.2 sde systems
　4.8 impact of noise on dynamics
　4.9 simulation
　4.10 problems
chapter 5 deterministic quantities for stochastic dynamics
　5.1 moments
　5.2 probability density functions
　　5.2.1 scalar fokker-planck equations
　　5.2.2 multidimensional fokker-planck equations
　　5.2.3 existence and uniqueness for fokker-planck equations
　　5.2.4 likelihood for transitions between different dynamical regimes under
　　uncertainty
　5.3 most probable phase portraits
　　5.3.1 mean phase portraits
　　5.3.2 almost sure phase portraits
　　5.3.3 most probable phase portraits
 5.4 mean exit time
 5.5 escape probability
 5.6 problems
chapter 6 invariant structures for stochastic dynamics
　6.1 deterministic dynamical systems
　　6.1.1 concepts for deterministic dynamical systems
　　6.1.2 the haxtman-grobman theorem
　　6.1.3 invariant sets
　　6.1.4 differentiable manifolds
　　6.1.5 deterministic invariant manifolds
　6.2 measurable dynamical systems
　6.3 random dynamical systems
　　6.3.1 canonical sample spaces for sdes
　　6.3.2 wiener shift

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