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| DYNAMICS OF STOCHASTIC SYSTEMS
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By
Valery Klyatskin, 1988 Research Professor of Theoretical and Mathematical Physics, Russian Academy of Science; 1977 D. Sc. in Physical and Mathematical
Sciences, Acoustical Institute, Russian Academy of Science; 1968 Ph.D. in Physical and Mathematical Sciences, Institute of Atmospheric
Physics Russian Academy of Science; 1964 M.Sc. in Theoretical Physics, Moscow Institute of Physics and Technology (FIZTEX)., Russian
Academy of Science, Russia
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.
Audience
Researchers in physics (fluid dynamics, optics, acoustics, radiophysics), geosciences (ocean, atmosphere physics), applied mathematics
(stochastic equations), applications (coherent phenomena). Senior and postgraduate students in different areas of physics, engineering
and applied mathematics.
Contents
Contents
Preface
Introduction
I Dynamical description of stochastic systems
1 Examples, basic problems, peculiar features of solutions
1.1 Ordinary differential equations: initial value problems
1.1.1 Particles under the random velocity field
1.1.2 Systems with blow-up
singularities
1.1.3 Oscillator with randomly varying frequency (stochastic parametric resonance)
1.2 Boundary-value problems for linear
ordinary differential equations (plane waves in layered media)
1.3 Partial differential equations
1.3.1 Passive tracer in random velocity
field
1.3.2 Quasilinear and nonlinear first-order partial differential equations
1.3.3 Parabolic equation of quasioptics (waves in
randomly inhomogeneous media)
1.3.4 Navier?Stokes equation: random forces in hydrodynamic theory of turbulence
2 Solution dependence
on problem type, medium parameters, and initial data
2.1 Functional representation of problem solution
2.1.1 Variational (functional)
derivatives
2.1.2 Principle of dynamic causality
2.2 Solution dependence on problem?s parameters
2.2.1 Solution dependence on initial
data
2.2.2 Imbedding method for boundary-value problems Problems
3 Indicator function and Liouville equation 42
3.1 Ordinary differential
equations
3.2 First-order partial differential equations
3.2.1 Linear equations
3.2.2 Quasilinear equations
3.2.3 General-form nonlinear
equations
3.3 Higher-order partial differential equations
3.3.1 Parabolic equation of quasioptics
3.3.2 Random forces in hydrodynamic
theory of turbulence
Problems
II Statistical description of stochastic systems
4 Random quantities, processes and fields
4.1 Random
quantities and their characteristics
4.2 Random processes, fields, and their characteristics
4.2.1 General remarks
4.2.2 Statistical
topography of random processes and fields4.2.3 Gaussian random process
4.2.4 Discontinuous random processes
4.3 Markovian processes
4.3.1 General properties
4.3.2 Characteristic functional of the Markovian process
Problems
5 Correlation splitting
5.1 General remarks
5.2 Gaussian process
5.3 Poisson process
5.4 Telegrapher?s random process
5.5 Delta-correlated random processes
5.5.1 Asymptotic meaning
of delta-correlated processes and fields
Problems
6 General approaches to analyzing stochastic dynamic systems
6.1 Ordinary differential
equations
6.2 Completely solvable stochastic dynamic systems
6.2.1 Ordinary differential equations
6.2.2 Partial differential equations
6.3 Delta-correlated fields and processes
6.3.1 One-dimensional nonlinear differential equation
6.3.2 Linear operator equation
Problems
7 Stochastic equations with the Markovian fluctuations of parameters
7.1 Telegrapher?s processes
7.2 Gaussian Markovian processes
Problems
8 Gaussian delta-correlated random field (ordinary differential equations)
8.1 The Fokker-Planck equation
8.2 Transition probability
distributions
8.3 Applicability range of the Fokker?Planck equation
8.3.1 Langevin equation
8.3.2 Diffusion approximation
Problems
9 Methods for solving and analyzing the Fokker-Planck equation
9.1 Wiener random process
9.2 Logarithmic-normal random process
9.3
Integral transformations
9.4 Steady-state solutions of the Fokker?Planck equation
9.4.1 One-dimensional nonlinear differential equation
9.4.2 Hamiltonian systems
9.5 Boundary-value problems for the Fokker-Planck equation (transfer phenomena)
9.6 Method of fast oscillation
averaging
Problems
10 Gaussian delta-correlated random field (causal integral equations)
Problems
III Examples of coherent phenomena
in stochastic dynamic systems
11 Passive tracer clustering and diffusion in random hydrodynamic flows
11.1 Lagrangian description (particle
diffusion)
11.1.1 One-point statistical characteristics
11.1.2 Two-point statistical characteristics
11.2 Diffusion of passive tracer
concentration in random velocity field
11.3 Effect of molecular diffusion
Problems
12 Wave localization in randomly layered media
12.1 Statistics of scattered field at layer boundaries
12.1.1 Reflection and transmission coefficients
12.1.2 Source inside the layer
of a medium
12.1.3 Statistical energy localization
12.2 Statistical theory of radiative transfer
12.2.1 Normal wave incidence on the
layer of random media
12.2.2 Plane wave source located in random medium
12.3 Numerical simulation
Problems
Bibliography
Index
| Bibliographic details |
Paperback, 212 pages, publication date: MAR-2005
ISBN-13: 978-0-444-51796-8
ISBN-10: 0-444-51796-0
Imprint: ELSEVIER
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USD 66.95
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