隨機動力係統導論（英文） 段金橋 科學齣版社 pdf epub mobi txt 電子書 下載 2025

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段金橋 著

店鋪： 科學齣版社旗艦店

齣版社： 科學齣版社

ISBN：9787030438577

版次：1

商品編碼：1750907291

包裝：精裝

齣版時間：2015-04-01

基本信息

書名：隨機動力係統導論（英文）

作者：段金橋

齣版社：科學齣版社

齣版日期：2015-04-01

ISBN：9787030438577

字數：373000

頁碼：300

版次：1

裝幀：精裝

開本：16開

商品重量：0.4kg

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內容提要

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隨機動力係統是一個入門較難的新興領域。段金 橋、楊樂編著的《隨機動力係統導論(英文版)(精)/ 純粹數學與應用數學專著》是這個領域的一個較為通 俗易懂的引論。在本書的第一部分，作者從簡單的隨 機動力係統實際例子齣發，引導讀者迴顧概率論和白 噪聲的基本知識，深入淺齣地介紹隨機微積分，然後 自然地展開隨機微分方程的討論。

目錄

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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
6.3.3 Cocycles and random dynamical systems
6.3.4 Examples of cocycles
6.3.5 Structural stability and stationary orbits
6.4 Linear stochastic dynamics
6.4.1 Oseledets' multiplicative ergodic theorem and Lyapunov exponents'
6.4.2 A stochastic Hartman-Grobman theorem
6.5* Random invariant manifolds
6.5.1 Definition of random invariant manifolds
6.5.2 Converting SDEs to RDEs
6.5.3 Local random pseudo-stable and pseudo-unstable manifolds
6.5.4 Local random stable, unstable and center manifolds
6.6 Problems
Chapter 7 Dynamical Systems Driven by Non-Gaussian Levy
Motions
7.1 Modeling via stochastic differential equations with Levy motions
7.2 Levy motions
7.2.1 Functions that have one-side limits
7.2.2 Levy-Ito deposition
7.2.3 Levy-Khintchine formula
7.2.4 Basic properties of Levy motions
7.3 s-stable Levy motions
7.3.1 Stable random variables
7.3.2 a-stable Levy motions in R1
7.3.3 a-stable Levy motion in Rn
7.4 Stochastic differential equations with Levy motions
7.4.1 Stochastic integration with respect to Levy motions
7.4.2 SDEs with Levy motions
7.4.3 Generators for SDEs with Levy motion
7.5 Mean exit time
7.5.1 Mean exit time for a-stable Levy motion
7.5.2 Mean exit time for SDEs with a-stable Levy motion
7.6 Escape probability and transition phenomena
7.6.1 Balayage-Dirichlet problem for escape probability
7.6.2 Escape probability for a-stable Levy motion
7.6.3 Escape probability for SDEs with a-stable Levy motion
7.7 Fokker-Planck equations
7.7.1 Fokker-Planck equations in R1
7.7.2 Fokker-Planck equations in Rn
7.8 Problems
Hints and Solutions
Further Readings
References
Index
Color Pictures

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