Generalized polynomial chaos for nonlinear random pantograph equations

This paper is concerned with the application of generalized polynomial chaos (gPC) method to nonlinear random pantograph equations. An error estimation of gPC method is derived. The global error analysis is given for the error arising from finite-dimensional noise (FDN) assumption, projection error, aliasing error and discretization error. In the end, with several numerical experiments, the theoretical results are further illustrated.     

Distributional chaos in random dynamical systems

In this paper, we introduce the notion of distributional chaos and the measure of chaos for random dynamical systems generated by two interval maps. We give some sufficient conditions for a zero measure of chaos and examples of chaotic systems. We demonstrate that the chaoticity of the functions that generate a system does not, in general, affect the chaoticity of the system, i.e. a chaotic system can arise from two nonchaotic functions and vice versa. Finally, we show that distributional chaos for random dynamical system is, in some sense, unstable.     

Polynomial chaos for multirate partial differential algebraic equations with random parameters

In radio frequency applications, a multivariate model yields an efficient representation of signals with amplitude modulation and/or frequency modulation. Periodic boundary value problems of multirate partial differential algebraic equations (MPDAEs) have to be solved to reproduce the quasiperiodic signals. Typically, technical parameters appear in the system, which may exhibit some uncertainty. Substitution by random variables results in a corresponding stochastic model. We apply the technique of the generalised polynomial chaos to obtain according solutions. A Galerkin approach yields larger coupled systems of MPDAEs. We analyse the properties of the coupled systems with respect to the original formulations. Thereby, we focus on the case of frequency modulation, since the case of amplitude modulation alone is straightforward.     

Estimates of solutions of some systems of differential equations with large retardation

The behavior of the solutions of differential equations with asymptotically large retardation is studied. In the case of linear systems with bounded coefficients and a class of nonlinear differential equations, estimates are derived which depend explicitly on the retardation.Translated from Matematicheskie Zametki, Vol. 6, No. 4, pp. 483–488, October, 1969.     

Application of chaos degree to some dynamical systems

Chaos degree defined through two complexities in information dynamics is applied to some deterministic dynamical models. It is shown that this degree well describes the chaotic feature of the models.     

A unified approach to the large deviations for small perturbations of random evolution equations

LetX ^ɛ = {X ^ɛ (t ; 0 ⩽t ⩽ 1 } (ɛ > 0) be the processes governed by the following stochastic differential equations:

Dependence of polynomial chaos on random types of forces of KdV equations

In this study, one-dimensional stochastic Korteweg–de Vries equation with uncertainty in its forcing term is considered. Extending the Wiener chaos expansion, a numerical algorithm based on orthonormal polynomials from the Askey scheme is derived. Then dependence of polynomial chaos on the distribution type of the random forcing term is inspected. It is numerically shown that when Hermite (Laguerre or Jacobi) polynomial chaos is chosen as a basis in the Gaussian (Gamma or Beta, respectively) random space for uncertainty, the solution to the KdV equation converges exponentially. If a proper polynomial chaos is not used, however, the solution converges with slower rate.     

Tensor methods for large sparse systems of nonlinear equations

This paper introduces tensor methods for solving large sparse systems of nonlinear equations. Tensor methods for nonlinear equations were developed in the context of solving small to medium-sized dense problems. They base each iteration on a quadratic model of the nonlinear equations, where the second-order term is selected so that the model requires no more derivative or function information per iteration than standard linear model-based methods, and hardly more storage or arithmetic operations per iteration. Computational experiments on small to medium-sized problems have shown tensor methods to be considerably more efficient than standard Newton-based methods, with a particularly large advantage on singular problems. This paper considers the extension of this approach to solve large sparse problems. The key issue considered is how to make efficient use of sparsity in forming and solving the tensor model problem at each iteration. Accomplishing this turns out to require an entirely new way of solving the tensor model that successfully exploits the sparsity of the Jacobian, whether the Jacobian is nonsingular or singular. We develop such an approach and, based upon it, an efficient tensor method for solving large sparse systems of nonlinear equations. Test results indicate that this tensor method is significantly more efficient and robust than an efficient sparse Newton-based method, in terms of iterations, function evaluations, and execution time. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.Work supported by the Mathematical, Information, and Computational Sciences Division subprogram of the Office of Computational and Technology Research, US Department of Energy, under Contract W-31-109-Eng-38, by the National Aerospace Agency under Purchase Order L25935D, and by the National Science Foundation, through the Center for Research on Parallel Computation, under Cooperative Agreement No. CCR-9120008.Research supported by AFOSR Grants No. AFOSR-90-0109 and F49620-94-1-0101, ARO Grants No. DAAL03-91-G-0151 and DAAH04-94-G-0228, and NSF Grant No. CCR-9101795.     

Homogenization of parabolic equations with large time-dependent random potential

This paper concerns the homogenization problem of a parabolic equation with large, time-dependent, random potentials in high dimensions d≥3$d\ge 3$. Depending on the competition between temporal and spatial mixing of the randomness, the homogenization procedure turns to be different. We characterize the difference by proving the corresponding weak convergence of Brownian motion in random scenery. When the potential depends on the spatial variable macroscopically, we prove a convergence to SPDE.     

Periodic Orbits for Some Systems of Delay Differential Equations

We provide sufficient conditions for the existence of periodic orbits of some systems of delay differential equations with a unique delay. We extend Kaplan-Yorke＇s method for finding periodic orbits from a delay differential equation with several delays to a system of delay differential equations with a unique delay.     

A parallel algorithm for large systems of Volterra integral equations of Abel type

A significative number of recent applications require numerical solution of large systems of Abel–Volterra integral equations. Here we propose a parallel algorithm to numerically solve a class of these systems, designed for a distributed-memory MIMD architecture. In order to achieve a good efficiency we employ a fully parallel and fast convergent waveform relaxation (WR) method and evaluate the lag term by using FFT techniques. To accelerate the convergence of the WR method and to best exploit the parallel architecture we develop special strategies. The performances of the resulting code, NSWR4, are illustrated on some examples.     

A bidiagonalization algorithm for solving large and sparse ill-posed systems of linear equations

An iterative method based on Lanczos bidiagonalization is developed for computing regularized solutions of large and sparse linear systems, which arise from discretizations of ill-posed problems in partial differential or integral equations. Determination of the regularization parameter and termination criteria are discussed. Comments are given on the computational implementation of the algorithm.Dedicated to Peter Naur on the occasion of his 60th birthday     

On some inverse problems for elliptic equations and systems

Under study are the inverse problems of determining the right-hand side of a particular form and the solution for elliptic systems, including a series of elasticity systems. (On the boundary of the domain the solution satisfies either the Dirichlet conditions or mixed Dirichlet-Neumann conditions.) We assume that on a system of planes the normal derivatives of the solution can have discontinuities of the first kind. The conjugating boundary conditions on the discontinuity surface are analogous to the continuity conditions for the fields of displacements and stresses for a horizontally laminated medium. The overdetermination conditions are integral (the average of the solution over some domain is specified) or local (the values of the solution on some lines are specified). We study the solvability conditions for these problems and their Fredholm property.     

A class of systems of ordinary differential equations of large dimension

We consider the Cauchy problem for a class of systems of ordinary differential equations of large dimension.We prove that, for sufficiently large number of equations, the last component of a solution to the Cauchy problem is an approximate solution to the initial value problem for a delay differential equation. Estimates of the approximation are obtained.