Stochastic Processes in Dynamics PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Stochastic Processes in Dynamics PDF full book. Access full book title Stochastic Processes in Dynamics by B. Skalmierski. Download full books in PDF and EPUB format.
Author: B. Skalmierski
Publisher: Springer Science & Business Media
ISBN: 9789024726868
Category : Science
Languages : en
Pages : 166
Get Book Here
Book Description
Author: B. Skalmierski
Publisher: Springer Science & Business Media
ISBN: 9789024726868
Category : Science
Languages : en
Pages : 166
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: American Society of Mechanical Engineers
Publisher:
ISBN:
Category : Machinery, Dynamics of
Languages : en
Pages :
Get Book Here
Book Description
Author: American Society of Mechanical Engineers Staff
Publisher:
ISBN: 9780608131672
Category :
Languages : en
Pages : 121
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Josef Honerkamp
Publisher: John Wiley & Sons
ISBN: 9780471188346
Category : Mathematics
Languages : de
Pages : 558
Get Book Here
Book Description
This unique volume introduces the reader to the mathematical language for complex systems and is ideal for students who are starting out in the study of stochastical dynamical systems. Unlike other books in the field it covers a broad array of stochastic and statistical methods.
Author:
Publisher:
ISBN:
Category : Machinery, Dynamics of
Languages : en
Pages : 115
Get Book Here
Book Description
Author: Valery I. Klyatskin
Publisher: Elsevier
ISBN: 008050485X
Category : Science
Languages : en
Pages : 211
Get Book Here
Book Description
Fluctuating parameters appear in a variety of physical systems and phenomena. They typically come either as random forces/sources, or advecting velocities, or media (material) parameters, like refraction index, conductivity, diffusivity, etc. The well known example of Brownian particle suspended in fluid and subjected to random molecular bombardment laid the foundation for modern stochastic calculus and statistical physics. Other important examples include turbulent transport and diffusion of particle-tracers (pollutants), or continuous densities (''oil slicks''), wave propagation and scattering in randomly inhomogeneous media, for instance light or sound propagating in the turbulent atmosphere. Such models naturally render to statistical description, where the input parameters and solutions are expressed by random processes and fields. The fundamental problem of stochastic dynamics is to identify the essential characteristics of system (its state and evolution), and relate those to the input parameters of the system and initial data. This raises a host of challenging mathematical issues. One could rarely solve such systems exactly (or approximately) in a closed analytic form, and their solutions depend in a complicated implicit manner on the initial-boundary data, forcing and system's (media) parameters . In mathematical terms such solution becomes a complicated "nonlinear functional" of random fields and processes. Part I gives mathematical formulation for the basic physical models of transport, diffusion, propagation and develops some analytic tools. Part II sets up and applies the techniques of variational calculus and stochastic analysis, like Fokker-Plank equation to those models, to produce exact or approximate solutions, or in worst case numeric procedures. The exposition is motivated and demonstrated with numerous examples. Part III takes up issues for the coherent phenomena in stochastic dynamical systems, described by ordinary and partial differential equations, like wave propagation in randomly layered media (localization), turbulent advection of passive tracers (clustering). Each chapter is appended with problems the reader to solve by himself (herself), which will be a good training for independent investigations. · This book is translation from Russian and is completed with new principal results of recent research.· The book develops mathematical tools of stochastic analysis, and applies them to a wide range of physical models of particles, fluids, and waves.· Accessible to a broad audience with general background in mathematical physics, but no special expertise in stochastic analysis, wave propagation or turbulence
Author: Kurt Jacobs
Publisher: Cambridge University Press
ISBN: 1139486799
Category : Science
Languages : en
Pages : 203
Get Book Here
Book Description
Stochastic processes are an essential part of numerous branches of physics, as well as in biology, chemistry, and finance. This textbook provides a solid understanding of stochastic processes and stochastic calculus in physics, without the need for measure theory. In avoiding measure theory, this textbook gives readers the tools necessary to use stochastic methods in research with a minimum of mathematical background. Coverage of the more exotic Levy processes is included, as is a concise account of numerical methods for simulating stochastic systems driven by Gaussian noise. The book concludes with a non-technical introduction to the concepts and jargon of measure-theoretic probability theory. With over 70 exercises, this textbook is an easily accessible introduction to stochastic processes and their applications, as well as methods for numerical simulation, for graduate students and researchers in physics.
Author: Andrew H. Jazwinski
Publisher: Courier Corporation
ISBN: 0486318192
Category : Science
Languages : en
Pages : 404
Get Book Here
Book Description
This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probability theory and stochastic processes, the author introduces and defines the problems of filtering, prediction, and smoothing. He presents the mathematical solutions to nonlinear filtering problems, and he specializes the nonlinear theory to linear problems. The final chapters deal with applications, addressing the development of approximate nonlinear filters, and presenting a critical analysis of their performance.
