Central Limits and Homogenization in Random Media
03 Jul 2008-Multiscale Modeling & Simulation (Society for Industrial and Applied Mathematics)-Vol. 7, Iss: 2, pp 677-702
TL;DR: In this paper, the rescaled difference between the perturbed and unperturbed solutions may be written asymptotically as explicit Gaussian processes, and the results are derived for more general elliptic equations with random coefficients in one dimension of space.
Abstract: We consider the perturbation of elliptic pseudodifferential operators $P(\mathbf{x},\mathbf{D})$ with more than square integrable Green's functions by random, rapidly varying, sufficiently mixing, potentials of the form $q(\frac{\mathbf{x}}{\varepsilon},\omega)$. We analyze the source and spectral problems associated with such operators and show that the rescaled difference between the perturbed and unperturbed solutions may be written asymptotically as $\varepsilon \to 0$ as explicit Gaussian processes. Such results may be seen as central limit corrections to homogenization (law of large numbers). Similar results are derived for more general elliptic equations with random coefficients in one dimension of space. The results are based on the availability of a rapidly converging integral formulation for the perturbed solutions and on the use of classical central limit results for random processes with appropriate mixing conditions.
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TL;DR: Derivations are based on a careful analysis of random oscillatory integrals of processes with long-range correlations and it is shown that the longer the range of the correlations, the larger is the amplitude of the corrector.
Abstract: This paper concerns the homogenization of a one-dimensional elliptic equation with oscillatory random coefficients. It is well-known that the random solution to the elliptic equation converges to the solution of an effective medium elliptic equation in the limit of a vanishing correlation length in the random medium. It is also well-known that the corrector to homogenization, i.e., the difference between the random solution and the homogenized solution, converges in distribution to a Gaussian process when the correlations in the random medium are sufficiently short-range. Moreover, the limiting process may be written as a stochastic integral with respect to standard Brownian motion. We generalize the result to a large class of processes with long-range correlations. In this setting, the corrector also converges to a Gaussian random process, which has an interpretation as a stochastic integral with respect to fractional Brownian motion. Moreover, we show that the longer the range of the correlations, the larger is the amplitude of the corrector. Derivations are based on a careful analysis of random oscillatory integrals of processes with long-range correlations. We also make use of the explicit expressions for the solutions to the one-dimensional elliptic equation.
89 citations
TL;DR: A novel and efficient strategy, which is entirely done by using the fast Fourier transform, is proposed to reconstruct the mean and the variance of the random source function from measurements at one boundary point, where the measurements are assumed to be available for many realizations of the source term.
Abstract: This paper is concerned with an inverse random source problem for the one-dimensional stochastic Helmholtz equation, which is to reconstruct the statistical properties of the random source function from boundary measurements of the radiating random electric field Although the emphasis of the paper is on the inverse problem, we adapt a computationally more efficient approach to study the solution of the direct problem in the context of the scattering model Specifically, the direct model problem is equivalently formulated into a two-point spatially stochastic boundary value problem, for which the existence and uniqueness of the pathwise solution is proved In particular, an explicit formula is deduced for the solution from an integral representation by solving the two-point boundary value problem Based on this formula, a novel and efficient strategy, which is entirely done by using the fast Fourier transform, is proposed to reconstruct the mean and the variance of the random source function from measurements at one boundary point, where the measurements are assumed to be available for many realizations of the source term Numerical examples are presented to demonstrate the validity and effectiveness of the proposed method
65 citations
TL;DR: The main result is to identify the correlation structure of the corrector, in dimension 33 and higher, which is similar to, but different from that of a Gaussian free field.
Abstract: Recently, the quantification of errors in the stochastic homogenization of divergence-form operators has witnessed important progress. Our aim now is to go beyond error bounds, and give precise descriptions of the effect of the randomness, in the large-scale limit. This paper is a first step in this direction. Our main result is to identify the correlation structure of the corrector, in dimension 33 and higher. This correlation structure is similar to, but different from that of a Gaussian free field.
53 citations
TL;DR: In this article, the authors identify the correlation structure of the corrector, in dimension $3$ and higher, which is similar to but different from that of a Gaussian free field.
Abstract: Recently, the quantification of errors in the stochastic homogenization of divergence-form operators has witnessed important progress. Our aim now is to go beyond error bounds, and give precise descriptions of the effect of the randomness, in the large-scale limit. This paper is a first step in this direction. Our main result is to identify the correlation structure of the corrector, in dimension $3$ and higher. This correlation structure is similar to, but different from that of a Gaussian free field.
42 citations
TL;DR: In this paper, a new derivation and an analysis of long-wave model equations for the dynamics of the free surface of a body of water which has random bathymetry is given.
Abstract: This paper gives a new derivation and an analysis of long-wave model equations for the dynamics of the free surface of a body of water which has random bathymetry. This is a problem of hydrodynamical significance to coastal regions and to global-scale propagation of tsunamis, for which there may be imperfect knowledge of the detailed topography of the bottom. The surface motion is assumed to be in a long-wavelength dynamical regime, while the bottom of the fluid region is given by a stationary random process whose realizations vary over short length scales and are decorrelated on the longer principal length scale of the surface waves. Our basic conclusions are that coherent solutions propagating over a random bottom maintain basic properties of their structure over long distances, but however, the effect of the random bottom introduces uncertainty in the location of the solution profile and modifies the amplitude by random factors. It also gives rise to a random scattered component of the solution, but this does not result in the dispersion of the principal component of the solution, at least over length and time scales considered in this regime. We illustrate these results with numerical simulations.The mathematical question is one of homogenization theory in the long-wave scaling regime, for which our work is a reappraisal of the paper of Rosales & Papanicolaou (Stud. Appl. Math., vol. 68, 1983, pp. 89–102). In particular, we derive appropriate Boussinesq and Korteweg–deVries type equations with random coefficients which describe the free-surface evolution in this regime. The derivation is performed from the point of view of perturbation theory for Hamiltonian partial differential equations with a small parameter, with a subsequent analysis of the random effects in the resulting solutions. In the analysis, we highlight the distinction between the effective equations for a fixed typical realization, for which there are coherent solitary-wave solutions, and their ensemble average, which may exhibit diffusive effects. Our results extend the prior analysis to the case of non-zero variance σ2β > 0, and furthermore the analysis identifies the canonical limit random process as a white noise with covariance σβ2δ(X − X′) and quantifies the variations in phase and amplitude of the principal and scattered components of solutions. We find that the random topography can give rise to an additional linear term in the KdV limit equations, which depends upon a skew property of the random process and whose sign affects the stability of solutions. Finally we generalize this analysis to the case in which the bottom has large-scale deterministic variations on which are superposed random fluctuations with slowly varying statistical properties.
40 citations
References
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Book•
01 Jan 1966
TL;DR: The monograph by T Kato as discussed by the authors is an excellent reference work in the theory of linear operators in Banach and Hilbert spaces and is a thoroughly worthwhile reference work both for graduate students in functional analysis as well as for researchers in perturbation, spectral, and scattering theory.
Abstract: "The monograph by T Kato is an excellent textbook in the theory of linear operators in Banach and Hilbert spaces It is a thoroughly worthwhile reference work both for graduate students in functional analysis as well as for researchers in perturbation, spectral, and scattering theory
In chapters 1, 3, 5 operators in finite-dimensional vector spaces, Banach spaces and Hilbert spaces are introduced Stability and perturbation theory are studied in finite-dimensional spaces (chapter 2) and in Banach spaces (chapter 4) Sesquilinear forms in Hilbert spaces are considered in detail (chapter 6), analytic and asymptotic perturbation theory is described (chapter 7 and 8) The fundamentals of semigroup theory are given in chapter 9 The supplementary notes appearing in the second edition of the book gave mainly additional information concerning scattering theory described in chapter 10
The first edition is now 30 years old The revised edition is 20 years old Nevertheless it is a standard textbook for the theory of linear operators It is user-friendly in the sense that any sought after definitions, theorems or proofs may be easily located In the last two decades much progress has been made in understanding some of the topics dealt with in the book, for instance in semigroup and scattering theory However the book has such a high didactical and scientific standard that I can recomment it for any mathematician or physicist interested in this field
Zentralblatt MATH, 836
19,846 citations
Book•
01 Jan 1968
TL;DR: Weak Convergence in Metric Spaces as discussed by the authors is one of the most common modes of convergence in metric spaces, and it can be seen as a form of weak convergence in metric space.
Abstract: Weak Convergence in Metric Spaces. The Space C. The Space D. Dependent Variables. Other Modes of Convergence. Appendix. Some Notes on the Problems. Bibliographical Notes. Bibliography. Index.
13,153 citations
Book•
04 Apr 1986
TL;DR: In this paper, the authors present a flowchart of generator and Markov Processes, and show that the flowchart can be viewed as a branching process of a generator.
Abstract: Introduction. 1. Operator Semigroups. 2. Stochastic Processes and Martingales. 3. Convergence of Probability Measures. 4. Generators and Markov Processes. 5. Stochastic Integral Equations. 6. Random Time Changes. 7. Invariance Principles and Diffusion Approximations. 8. Examples of Generators. 9. Branching Processes. 10. Genetic Models. 11. Density Dependent Population Processes. 12. Random Evolutions. Appendixes. References. Index. Flowchart.
5,560 citations
Book•
20 Dec 1990TL;DR: In this article, a representation of stochastic processes and response statistics are represented by finite element method and response representation, respectively, and numerical examples are provided for each of them.
Abstract: Representation of stochastic processes stochastic finite element method - response representation stochastic finite element method - response statistics numerical examples.
5,495 citations
Book•
31 Dec 1980
TL;DR: In this article, the authors define the boundedness in probability and stability of Stochastic Processes Defined by Differential Equations (SDEs) defined by Markov Processes.
Abstract: Boundedness in Probability and Stability of Stochastic Processes Defined by Differential Equations.- 2.Stationary and Periodic Solutions of Differential Equations. 3.Markov Processes and Stochastic Differential Equations.- 4.Ergodic Properties of Solutions of Stochastic Equations.- 5.Stability of Stochastic Differential Equations.- 6.Systems of Linear Stochastic Equations.- 7.Some Special Problems in the Theory of Stability of SDE's.- 8.Stabilization of Controlled Stochastic Systems.- A. Appendix to the First English Edition.- B. Appendix to the Second Edition. Moment Lyapunov Exponents and Stability Index.- References.- Index.
2,944 citations