Understanding Complex Systems Founding Editor Prof. Dr. J.A. Scott Kelso Center for Complex Systems & Brain Sciences Florida Atlantic University Boca Raton FL, USA E-mail: [email protected]Editorial and Programme Advisory Board Dan Braha New England Complex Systems, Institute and University of Massachusetts, Dartmouth Péter Érdi Center for Complex Systems Studies, Kalamazoo College, USA and Hungarian Academy of Sciences, Budapest, Hungary Karl Friston Institute of Cognitive Neuroscience, University College London, London, UK Hermann Haken Center of Synergetics, University of Stuttgart, Stuttgart, Germany Viktor Jirsa Centre National de la Recherche Scientifique (CNRS), Université de la Méditerranée, Marseille, France Janusz Kacprzyk System Research, Polish Academy of Sciences, Warsaw, Poland Kunihiko Kaneko Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, Japan Scott Kelso Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, USA Markus Kirkilionis Mathematics Institute and Centre for Complex Systems, University of Warwick, Coventry, UK Jürgen Kurths Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany Andrzej Nowak Department of Psychology, Warsaw University, Poland Linda Reichl Center for Complex Quantum Systems, University of Texas, Austin, USA Peter Schuster Theoretical Chemistry and Structural Biology, University of Vienna, Vienna, Austria Frank Schweitzer System Design, ETH Zürich, Zürich, Switzerland Didier Sornette Entrepreneurial Risk, ETH Zürich, Zürich, Switzerland For further volumes: http://www.springer.com/series/5394
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Understanding Complex Systems
Founding Editor
Prof. Dr. J.A. Scott KelsoCenter for Complex Systems & Brain SciencesFlorida Atlantic UniversityBoca Raton FL, USAE-mail: [email protected]
Editorial and Programme Advisory Board
Dan BrahaNew England Complex Systems, Institute and University of Massachusetts, Dartmouth
Péter ÉrdiCenter for Complex Systems Studies, Kalamazoo College, USA and Hungarian Academy ofSciences, Budapest, Hungary
Karl FristonInstitute of Cognitive Neuroscience, University College London, London, UK
Hermann HakenCenter of Synergetics, University of Stuttgart, Stuttgart, Germany
Viktor JirsaCentre National de la Recherche Scientifique (CNRS), Université de la Méditerranée, Marseille,France
Janusz KacprzykSystem Research, Polish Academy of Sciences, Warsaw, Poland
Kunihiko KanekoResearch Center for Complex Systems Biology, The University of Tokyo, Tokyo, Japan
Scott KelsoCenter for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, USA
Markus KirkilionisMathematics Institute and Centre for Complex Systems, University of Warwick, Coventry, UK
Jürgen KurthsPotsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
Andrzej NowakDepartment of Psychology, Warsaw University, Poland
Linda ReichlCenter for Complex Quantum Systems, University of Texas, Austin, USA
Peter SchusterTheoretical Chemistry and Structural Biology, University of Vienna, Vienna, Austria
Frank SchweitzerSystem Design, ETH Zürich, Zürich, Switzerland
Didier SornetteEntrepreneurial Risk, ETH Zürich, Zürich, Switzerland
For further volumes:
http://www.springer.com/series/5394
Understanding Complex Systems
Future scientific and technological developments in many fields will necessarily depend upon comingto grips with complex systems. Such systems are complex in both their composition - typically manydifferent kinds of components interacting simultaneously and nonlinearly with each other and their envi-ronments on multiple levels - and in the rich diversity of behavior of which they are capable.
The Springer Series in Understanding Complex Systems series (UCS) promotes new strategies andparadigms for understanding and realizing applications of complex systems research in a wide variety offields and endeavors. UCS is explicitly transdisciplinary. It has three main goals: First, to elaborate theconcepts, methods and tools of complex systems at all levels of description and in all scientific fields,especially newly emerging areas within the life, social, behavioral, economic, neuro and cognitive sci-ences (and derivatives thereof); second, to encourage novel applications of these ideas in various fieldsof engineering and computation such as robotics, nano-technology and informatics; third, to provide asingle forum within which commonalities and differences in the workings of complex systems may bediscerned, hence leading to deeper insight and understanding.
UCS will publish monographs, lecture notes and selected edited contributions aimed at communicat-ing new findings to a large multidisciplinary audience.
Springer ComplexitySpringer Complexity is an interdisciplinary program publishing the best research and academic-levelteaching on both fundamental and applied aspects of complex systems - cutting across all traditional dis-ciplines of the natural and life sciences, engineering, economics, medicine, neuroscience, social and com-puter science.
Complex Systems are systems that comprise many interacting parts with the ability to generate a newquality of macroscopic collective behavior the manifestations of which are the spontaneous formation ofdistinctive temporal, spatial or functional structures. Models of such systems can be successfully mappedonto quite diverse “real-life” situations like the climate, the coherent emission of light from lasers, chem-ical reaction-diffusion systems, biological cellular networks, the dynamics of stock markets and of theinternet, earthquake statistics and prediction, freeway traffic, the human brain, or the formation of opin-ions in social systems, to name just some of the popular applications.
Although their scope and methodologies overlap somewhat, one can distinguish the following mainconcepts and tools: self-organization, nonlinear dynamics, synergetics, turbulence, dynamical systems,catastrophes, instabilities, stochastic processes, chaos, graphs and networks, cellular automata, adaptivesystems, genetic algorithms and computational intelligence.
The two major book publication platforms of the Springer Complexity program are the monographseries “Understanding Complex Systems” focusing on the various applications of complexity, and the“Springer Series in Synergetics”, which is devoted to the quantitative theoretical and methodological foun-dations. In addition to the books in these two core series, the program also incorporates individual titlesranging from textbooks to major reference works.
Valery I. Klyatskin
Stochastic Equations:Theory and Applications inAcoustics, Hydrodynamics,Magnetohydrodynamics,and Radiophysics, Volume 2
Coherent Phenomena inStochastic Dynamic Systems
Translated from Russianby A. Vinogradov
ABC
Valery I. KlyatskinA.M. Obukhov Institute of
Atmospheric PhysicsRussian Academy of SciencesMoscowRussia
ISSN 1860-0832 ISSN 1860-0840 (electronic)ISBN 978-3-319-07589-1 ISBN 978-3-319-07590-7 (eBook)DOI 10.1007/978-3-319-07590-7Springer Cham Heidelberg New York Dordrecht London
Springer is part of Springer Science+Business Media (www.springer.com)
5
To Sonya Klyatskina
Preface
This monograph is revised and more comprehensive translation of my Rus-sian monographs [154] and in 2 volumes [155, 156]. For reasons of usability,the material is divided into two practically independent volumes. In Volume2, general methods developed in Volume 1 are used to consider the the-ory of coherent phenomena occurring in stochastic dynamic systems with aprobability one, i.e., almost in all realizations of a random process. Amongthe examined phenomena are clustering of particles and passive scalar tracerfield (density field) in random velocity field, passive vector tracer field (mag-netic field) in a random velocity field, dynamic localization of plane wavesin layered random media, propagation of monochromatic waves in randommedia, and formation of caustic structures in different-nature wavefields prop-agating in multidimensional random media (under the assumption that wavepropagation is described in terms of the scalar parabolic equation).
Diffusion and clustering of both inertialess and low-inertia passive scalartracer (density field) in different media and under different conditions areconsidered here in significantly greater detail on the basis of monograph [154].In addition, I added consideration of diffusion and clustering of magnetic fieldenergy in random media.
Working at this edition, I tried to take into account remarks and wishes ofreaders about both style of the text and choice of specific problems. Differ-ent mistakes and misprints are corrected. The book is destined for scientistsdealing with stochastic dynamic systems in different areas, such as hydro-dynamics, magnetohydrodynamics, acoustics, radiophysics, theoretical andmathematical physics, and applied mathematics, and can be useful for seniorand postgraduate students.
Volume 2 consists of five Parts and one Appendix.Part I of this volume deals with the problem of stochastic structure forma-
tion in random hydrodynamic flows. In particular, starting from an analysisof the steady-state probability density, it considers coherent structures of vor-tex formation (vortex genesis) in stochastic quasi-geostrophic flows, which arerelated to rotation and random topography of the bottom. This Part includes
VIII Preface
also challenges of analyzing the possibility of formation of anomalously largewaves (rogue waves) on the sea surface in terms of stochastic topography.
Part II deals with diffusion and clustering of particles and passive scalartracer density (density field) in random hydrodynamic velocity field.
Part III deals with diffusion and clustering of passive vector field (mag-netic) in random magnetohydrodynamic velocity field.
Part IV considers the phenomenon of dynamic localization accompanyingplane wave propagation in layered random media, and
Part V considers statistical description of wavefields propagating in ran-dom multidimensional media including the problem of caustic structure for-mation. These problems are formulated in terms of both ordinary and partialdifferential equations, and every of them can be divided into the number ofseparate problems of independent physical interest.
Part VI – Appendices discusses in detail the derivation of equations ofthe imbedding method that offers a possibility of reformulating boundary-value wave problems (linear and nonlinear, stationary and nonstationary)into initial-value problems with respect to auxiliary variables.
It is worth noting that purely mathematical and physical papers devotedto considered issues run into thousands. It would be physically impossibleto give an exhaustive bibliography. Therefore, in this book I confine myselfto referencing the papers used or discussed in this book, recent reviews andpapers with extensive bibliography on the subject.
13.4.1 Wave Incident on the Medium Layer . . . . . . . . . . . . . . . 24513.4.2 Plane Wave Source in the Medium Layer . . . . . . . . . . . 24613.4.3 Nonlinear Problem on Wave Self-action in Random
. . .Chaos is the place which servesto contain all things; for if thishad not subsisted neither earth norwater nor the rest of the elements,nor the Universe as a whole, couldhave been constructed. . . .
Sextus Empiricus, Against thePhysics, against the Ethicists, R.G.Bury, p. 217, Harvard UniversityPress, 1997.
Introduction
In recent time, the interest of both theoreticians and experimenters has beenattracted to the relation between the behavior of average statistical characte-ristics of a problem solution and the behavior of the solution in certain hap-penings (realizations). This is especially important for geophysical problemsrelated to the atmosphere and ocean where, generally speaking, a respectiveaveraging ensemble is absent and experimenters, as a rule, have to do withindividual observations.
Seeking solutions to dynamic problems for these specific realizations ofmedium parameters is almost hopeless due to extreme mathematical com-plexity of these problems. At the same time, researchers are interested inmain characteristics of these phenomena without much need to know spe-cific details. Therefore, the idea to use a well developed approach to randomprocesses and fields based on ensemble averages rather than separate obser-vations proved to be very fruitful. By way of example, almost all physicalproblems of atmosphere and ocean to some extent are treated by statisticalanalysis.
Randomness in medium parameters gives rise to a stochastic behavior ofphysical fields. Individual samples of scalar two-dimensional fields ρ (R, t),R = (x, y), say, recall a rough mountainous terrain with randomly scatteredpeaks, troughs, ridges and saddles. Common methods of statistical averaging(computing mean-type averages — 〈ρ (R, t)〉, space-time correlation function—
⟨
ρ (R, t) ρ(
R′, t′)⟩
etc., where 〈· · · 〉 implies averaging over an ensembleof random parameter samples) smooth the qualitative features of specificsamples. Frequently, these statistical characteristics have nothing in commonwith the behavior of specific samples, and at first glance may even seemto be at variance with them. For example, the statistical averaging over all
XVI Introduction
observations makes the field of average concentration of a passive tracer in arandom velocity field ever more smooth, whereas each its realization sampletends to be more irregular in space due to mixture of areas with substantiallydifferent concentrations.
Thus, these types of statistical average usually characterize "global" space-time dimensions of the area with stochastic processes but tell no details aboutthe process behavior inside the area. For this case, details heavily depend onthe velocity field pattern, specifically, on whether it is divergent or solenoidal.Thus, the first case will show with the probability one that clusters will beformed, i.e. compact areas of enhanced concentration of tracer surroundedby vast areas of low-concentration tracer. In the circumstances, all statisticalmoments of the distance between the particles will grow with time exponen-tially; that is, on average, a statistical recession of particles will take place.
In a similar way, in case of waves propagating in random media, an ex-ponential spread of the rays will take place on average; but simultaneously,with the probability one, caustics will form at finite distances. One more ex-ample to illustrate this point is the dynamic localization of plane waves inlayered randomly inhomogeneous media. In this phenomenon, the wavefieldintensity exponentially decays inward the medium with the probability onewhen the wave is incident on the half-space of such a medium, while all sta-tistical moments increase exponentially with distance from the boundary ofthe medium.
These physical processes and phenomena occurring with the probabilityone will be referred to as coherent processes and phenomena [166]. This typeof statistical coherence may be viewed as some organization of a complexdynamic system, and retrieval of its statistically stable characteristics is sim-ilar to the concept of coherence as self-organization of multicomponent sys-tems that evolve from the random interactions of their elements [265]. In thegeneral case, it is rather difficult to say whether or not the phenomenon occurswith the probability one. However, for a number of applications amenable totreatment with the simple models of fluctuating parameters, this may be han-dled by analytical means. In other cases, one may verify this by performingnumerical modeling experiments or analyzing experimental findings.
For these reasons, I interpret the main problem of the statistical analysisof stochastic dynamic systems as revealing the fundamental features of suchsystems, which appear with probability one, i.e., almost in all realizations ofthe dynamic systems under consideration, on the basis of the correspondingstatistical analysis.
I note a curious fact that nontrivial situations can be realized even inGaussian random fields! Such a situation is realized, for example, in the two-dimensional problems of geophysical hydrodynamics in rotating liquids withrandom bottom topography (see Part 1).
The complete statistics (say, the whole body of all n-point space-timemoment functions), would undoubtedly contain all the information about theinvestigated dynamic system. In practice, however, one may succeed only in
Introduction XVII
studying the simplest statistical characteristics associated mainly with one-time and one-point probability distributions. It would be reasonable to askhow with these statistics on hand one would look into the quantitative andqualitative behavior of some system happenings?
This question is answered by methods of statistical topography (see, forexample [126]). These methods were highlighted by Ziman [336], who seemsto had coined this term. Statistical topography yields a different philosophy ofstatistical analysis of dynamic stochastic systems, which may prove useful forexperimenters planning a statistical processing of experimental data. Theseissues are treated in depths in this book.