Construction of Nonlinear Normal Modes by Shaw-Pierre via Schur Decomposition

In the paper the simplification of construction of nonlinear normal vibration modes by Shaw-Pierre in power series form is considered. The simplification can be obtained via change of variables in the equations o f motion of dynamical system under consideration. This change of variables is constructed by means of so-called ordered Schur matrix decomposition. As the result of the transformation there is no need in solving nonlinear algebraic equations in order to evaluate coefficients of nonlinear normal mode

System-theoretical algorithmic solution to waiting times in semi-Markov queues

Cataloged from PDF version of article.Markov renewal processes with matrix-exponential semi-Markov kernels provide a generic tool for modeling auto-correlated interarrival and service times in queueing systems. In this paper, we study the steady-state actual waiting time distribution in an infinite capacity single-server semi-Markov queue with the auto-correlation in interarrival and service times modeled by Markov renewal processes with matrix-exponential kernels. Our approach is based on the equivalence between the waiting time distribution of this semi-Markov queue and the output of a linear feedback interconnection system. The unknown parameters of the latter system need to be determined through the solution of a SDC (Spectral-Divide-and-Conquer) problem for which we propose to use the ordered Schur decomposition. This approach leads us to a completely matrix-analytical algorithm to calculate the steady-state waiting time which has a matrix-exponential distribution. Besides its unifying structure, the proposed algorithm is easy to implement and is computationally efficient and stable. We validate the effectiveness and the generality of the proposed approach through numerical examples. © 2009 Elsevier B.V. All rights reserve

A Tutorial on the Spectral Theory of Markov Chains

Markov chains are a class of probabilistic models that have achieved widespread application in the quantitative sciences. This is in part due to their versatility, but is compounded by the ease with which they can be probed analytically. This tutorial provides an in-depth introduction to Markov chains, and explores their connection to graphs and random walks. We utilize tools from linear algebra and graph theory to describe the transition matrices of different types of Markov chains, with a particular focus on exploring properties of the eigenvalues and eigenvectors corresponding to these matrices. The results presented are relevant to a number of methods in machine learning and data mining, which we describe at various stages. Rather than being a novel academic study in its own right, this text presents a collection of known results, together with some new concepts. Moreover, the tutorial focuses on offering intuition to readers rather than formal understanding, and only assumes basic exposure to concepts from linear algebra and probability theory. It is therefore accessible to students and researchers from a wide variety of disciplines

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a State-dependent Poisson Structure

We develop structure-preserving reduced basis methods for a large class of nondissipative problems by resorting to their formulation as Hamiltonian dynamical systems. With this perspective, the phase space is naturally endowed with a Poisson manifold structure which encodes the physical properties, symmetries, and conservation laws of the dynamics. The goal is to design reduced basis methods for the general state-dependent degenerate Poisson structure based on a two-step approach. First, via a local approximation of the Poisson tensor, we split the Hamiltonian dynamics into an "almost symplectic" part and the trivial evolution of the Casimir invariants. Second, canonically symplectic reduced basis techniques are applied to the nontrivial component of the dynamics, preserving the local Poisson tensor kernel exactly. The global Poisson structure and the conservation properties of the phase flow are retained by the reduced model in the constant-valued case and up to errors in the Poisson tensor approximation in the state-dependent case. A priori error estimates for the solution of the reduced system are established. A set of numerical simulations is presented to corroborate the theoretical findings

Dichtematrix-Renormierung, angewandt auf nichtlineare dynamische Systeme

Bogner T. Density matrix renormalisation applied to nonlinear dynamical systems. Bielefeld (Germany): Bielefeld University; 2007.In dieser Dissertation wird die effektive numerische Beschreibung nichtlinearer dynamischer Systeme untersucht. Systeme dieser Art tauchen praktisch überall auf, wo zeitabhängige Größen quantitativ untersucht werden, d.h. in fast allen Bereichen der Physik, aber auch in der Biologie, Ökonomie oder Mathematik. Ziel ist die Bestimmung reduzierter Modelle, deren Phasenraum eine signifikant reduzierte Dimensionalität aufweist. Dies wird erreicht durch Benutzung von Konzepten aus der Dichtematrix-Renormierung. In dieser Arbeit werden drei neue Anwendungen vorgeschlagen. Zuerst wird eine Dichtematrix-Renormierungsmethode zur Berechnung einer Schur-Zerlegung vorgestellt. Verglichen mit bereits existierenden Arbeiten liegt der Vorteil dieses Ansatzes in der Möglichkeit, auch für nicht-normale Operatoren orthonormale Basen von sukzessive invarianten Unterräumen zu bestimmen. Der Algorithmus wird dann angewandt auf Gittermodelle stochastischer Systeme, wobei als Beispiele ein Reaktions-Diffusions- und ein Oberflächenablagerungs-Modell dienen. Als Nächstes wird ein Dichtematrix-Renormierungsansatz für die orthogonale Zerlegung (proper orthogonal decomposition) entwickelt. Diese Zerlegung erlaubt die Bestimmung relevanter linearer Unterräume auch für nichtlineare Systeme. Durch die Verwendung der Dichtematrix-Renormierung werden alle Berechnungen nur für kleine Untersysteme durchgeführt. Dabei werden diskretisierte partielle Differentialgleichungen, d.h. die Diffusionsgleichung, die Burgers-Gleichung und eine nichtlineare Diffusionsgleichung als numerische Beispiele betrachtet. Schließlich wird das vorige Konzept auf höherdimensionale Probleme in Form eines Variationsverfahrens erweitert. Dies Verfahren wird dann an den zweidimensionalen Navier-Stokes-Gleichungen erprobt.In this work the effective numerical description of nonlinear dynamical systems is investigated. Such systems arise in most fields of physics, as well as in mathematics, biology, economy and essentially in all problems for which a quantitative description of a time evolution is considered. The aim is to find reduced models with a phase space of significantly reduced dimensionality. This is achieved by the use of concepts from density matrix renormalisation. Three new applications are proposed in this work. First, a density matrix renormalisation method for calculating a Schur decomposition is introduced. The advantage of this approach, compared to existing work, is the possibility to obtain orthonormal bases for successively invariant subspaces even if the generator of evolution is not normal. The algorithm is applied to lattice models for stochastic systems, namely a reaction diffusion and a surface deposition model. Next, a density matrix renormalisation approach to the proper orthogonal decomposition is developed. This allows the determination of relevant linear subspaces even for nonlinear systems. Due to the use of density matrix renormalisation concepts, all calculations are done on small subsystems. Here discretised partial differential equations, i.e. the diffusion equation, the Burgers equation and a nonlinear diffusion equation are considered as numerical examples. Finally, the previous concept is extended to higher dimensional problems in a variational form. This method is then applied to the two-dimensional, incompressible Navier-Stokes equations as testing ground