語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Analysis and data-based reconstructi...
~
SpringerLink (Online service)
Analysis and data-based reconstruction of complex nonlinear dynamical systemsusing the methods of stochastic processes /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Analysis and data-based reconstruction of complex nonlinear dynamical systemsby M. Reza Rahimi Tabar.
其他題名:
using the methods of stochastic processes /
作者:
Tabar, M. Reza Rahimi.
出版者:
Cham :Springer International Publishing :2019.
面頁冊數:
xviii, 280 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
Stochastic processes.
電子資源:
https://doi.org/10.1007/978-3-030-18472-8
ISBN:
9783030184728$q(electronic bk.)
Analysis and data-based reconstruction of complex nonlinear dynamical systemsusing the methods of stochastic processes /
Tabar, M. Reza Rahimi.
Analysis and data-based reconstruction of complex nonlinear dynamical systems
using the methods of stochastic processes /[electronic resource] :by M. Reza Rahimi Tabar. - Cham :Springer International Publishing :2019. - xviii, 280 p. :ill., digital ;24 cm. - Understanding complex systems,1860-0832. - Understanding complex systems..
1 Introduction -- 2 Introduction to Stochastic Processes -- 3 Kramers-Moyal Expansion and Fokker-Planck Equation -- 4 Continuous Stochastic Process -- 5 The Langevin Equation and Wiener Process -- 6 Stochastic Integration, It^o and Stratonovich Calculi -- 7 Equivalence of Langevin and Fokker-Planck Equations -- 8 Examples of Stochastic Calculus -- 9 Langevin Dynamics in Higher Dimensions -- 10 Levy Noise Driven Langevin Equation and its Time Series-Based Reconstruction -- 11 Stochastic Processes with Jumps and Non-Vanishing Higher-Order Kramers-Moyal Coefficients -- 12 Jump-Diffusion Processes -- 13 Two-Dimensional (Bivariate) Jump-Diffusion Processes -- 14 Numerical Solution of Stochastic Differential Equations: Diffusion and Jump-Diffusion Processes -- 15 The Friedrich-Peinke Approach to Reconstruction of Dynamical Equation for Time Series: Complexity in View of Stochastic Processes -- 16 How To Set Up Stochastic Equations For Real-World Processes: Markov-Einstein Time Scale -- 17 Reconstruction of Stochastic Dynamical Equations: Exemplary Stationary Diffusion and Jump-Diffusion Processes -- 18 The Kramers-Moyal Coefficients of Non-Stationary Time series in The Presence of Microstructure (Measurement) Noise -- 19 Influence of Finite Time Step in Estimating of the Kramers-Moyal Coefficients -- 20 Distinguishing Diffusive and Jumpy Behaviors in Real-World Time Series -- 21 Reconstruction of Langevin and Jump-Diffusion Dynamics From Empirical Uni- and Bivariate Time Series -- 22 Applications and Outlook -- 23 Epileptic Brain Dynamics.
This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni- or multi-variant sequentially measured set of experimental data (even noisy data), how should one analyse non-parametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation? Here, the term "non-parametrically" exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data. The book provides an overview of methods that have been developed for the analysis of fluctuating time series and of spatially disordered structures. Thanks to its feasibility and simplicity, it has been successfully applied to fluctuating time series and spatially disordered structures of complex systems studied in scientific fields such as physics, astrophysics, meteorology, earth science, engineering, finance, medicine and the neurosciences, and has led to a number of important results. The book also includes the numerical and analytical approaches to the analyses of complex time series that are most common in the physical and natural sciences. Further, it is self-contained and readily accessible to students, scientists, and researchers who are familiar with traditional methods of mathematics, such as ordinary, and partial differential equations. The codes for analysing continuous time series are available in an R package developed by the research group Turbulence, Wind energy and Stochastic (TWiSt) at the Carl von Ossietzky University of Oldenburg under the supervision of Prof. Dr. Joachim Peinke. This package makes it possible to extract the (stochastic) evolution equation underlying a set of data or measurements.
ISBN: 9783030184728$q(electronic bk.)
Standard No.: 10.1007/978-3-030-18472-8doiSubjects--Topical Terms:
181874
Stochastic processes.
LC Class. No.: QA274 / .T333 2019
Dewey Class. No.: 519.23
Analysis and data-based reconstruction of complex nonlinear dynamical systemsusing the methods of stochastic processes /
LDR
:04524nmm a2200349 a 4500
001
564035
003
DE-He213
005
20191213131507.0
006
m d
007
cr nn 008maaau
008
200311s2019 gw s 0 eng d
020
$a
9783030184728$q(electronic bk.)
020
$a
9783030184711$q(paper)
024
7
$a
10.1007/978-3-030-18472-8
$2
doi
035
$a
978-3-030-18472-8
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA274
$b
.T333 2019
072
7
$a
PHS
$2
bicssc
072
7
$a
SCI055000
$2
bisacsh
072
7
$a
PHS
$2
thema
072
7
$a
PHDT
$2
thema
082
0 4
$a
519.23
$2
23
090
$a
QA274
$b
.T112 2019
100
1
$a
Tabar, M. Reza Rahimi.
$3
849852
245
1 0
$a
Analysis and data-based reconstruction of complex nonlinear dynamical systems
$h
[electronic resource] :
$b
using the methods of stochastic processes /
$c
by M. Reza Rahimi Tabar.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2019.
300
$a
xviii, 280 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Understanding complex systems,
$x
1860-0832
505
0
$a
1 Introduction -- 2 Introduction to Stochastic Processes -- 3 Kramers-Moyal Expansion and Fokker-Planck Equation -- 4 Continuous Stochastic Process -- 5 The Langevin Equation and Wiener Process -- 6 Stochastic Integration, It^o and Stratonovich Calculi -- 7 Equivalence of Langevin and Fokker-Planck Equations -- 8 Examples of Stochastic Calculus -- 9 Langevin Dynamics in Higher Dimensions -- 10 Levy Noise Driven Langevin Equation and its Time Series-Based Reconstruction -- 11 Stochastic Processes with Jumps and Non-Vanishing Higher-Order Kramers-Moyal Coefficients -- 12 Jump-Diffusion Processes -- 13 Two-Dimensional (Bivariate) Jump-Diffusion Processes -- 14 Numerical Solution of Stochastic Differential Equations: Diffusion and Jump-Diffusion Processes -- 15 The Friedrich-Peinke Approach to Reconstruction of Dynamical Equation for Time Series: Complexity in View of Stochastic Processes -- 16 How To Set Up Stochastic Equations For Real-World Processes: Markov-Einstein Time Scale -- 17 Reconstruction of Stochastic Dynamical Equations: Exemplary Stationary Diffusion and Jump-Diffusion Processes -- 18 The Kramers-Moyal Coefficients of Non-Stationary Time series in The Presence of Microstructure (Measurement) Noise -- 19 Influence of Finite Time Step in Estimating of the Kramers-Moyal Coefficients -- 20 Distinguishing Diffusive and Jumpy Behaviors in Real-World Time Series -- 21 Reconstruction of Langevin and Jump-Diffusion Dynamics From Empirical Uni- and Bivariate Time Series -- 22 Applications and Outlook -- 23 Epileptic Brain Dynamics.
520
$a
This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni- or multi-variant sequentially measured set of experimental data (even noisy data), how should one analyse non-parametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation? Here, the term "non-parametrically" exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data. The book provides an overview of methods that have been developed for the analysis of fluctuating time series and of spatially disordered structures. Thanks to its feasibility and simplicity, it has been successfully applied to fluctuating time series and spatially disordered structures of complex systems studied in scientific fields such as physics, astrophysics, meteorology, earth science, engineering, finance, medicine and the neurosciences, and has led to a number of important results. The book also includes the numerical and analytical approaches to the analyses of complex time series that are most common in the physical and natural sciences. Further, it is self-contained and readily accessible to students, scientists, and researchers who are familiar with traditional methods of mathematics, such as ordinary, and partial differential equations. The codes for analysing continuous time series are available in an R package developed by the research group Turbulence, Wind energy and Stochastic (TWiSt) at the Carl von Ossietzky University of Oldenburg under the supervision of Prof. Dr. Joachim Peinke. This package makes it possible to extract the (stochastic) evolution equation underlying a set of data or measurements.
650
0
$a
Stochastic processes.
$3
181874
650
0
$a
Time-series analysis.
$3
181890
650
1 4
$a
Complex Systems.
$3
558544
650
2 4
$a
Probability Theory and Stochastic Processes.
$3
274061
650
2 4
$a
Economic Theory/Quantitative Economics/Mathematical Methods.
$3
731081
650
2 4
$a
Complexity.
$3
274400
650
2 4
$a
Neurosciences.
$3
211508
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
830
0
$a
Understanding complex systems.
$3
560200
856
4 0
$u
https://doi.org/10.1007/978-3-030-18472-8
950
$a
Physics and Astronomy (Springer-11651)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000175339
電子館藏
1圖書
電子書
EB QA274 .T112 2019 2019
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://doi.org/10.1007/978-3-030-18472-8
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入