Model reduction for large-scale dynamical systems via equality constrained least squares

In this paper, we present a new method of model reduction for large-scale dynamical systems, which belongs to the SVD-Krylov based method category. It is a two-sided projection where one side reflects the Krylov part and the other side reflects the SVD (observability gramian) part. The reduced model matches the first r+i Markov parameters of the full order model, and the remaining ones approximate in a least squares sense without being explicitly computed, where r is the order of the reduced system, and i is a nonnegative integer such that 1≤i<r. The reduced system minimizes a weighted ?₂ error. By the definition of a shift operator, the proposed approximation is also obtained by solving an equality constrained least squares problem. Moreover, the method is generalized for moment matching at arbitrary interpolation points. Several numerical examples verify the effectiveness of the approach.     

Newton's method and complex dynamical systems

This article is devoted to the discussion of Newton's method. Beginning with the old results of A.Cayley and E.Schröder we proceed to the theory of complex dynamical systems on the sphere, which was developed by G.Julia and P.Fatou at the beginning of this century, and continued by several mathematicians in recent years.     

Interpolatory model reduction of parameterized bilinear dynamical systems

Interpolatory projection methods for model reduction of nonparametric linear dynamical systems have been successfully extended to nonparametric bilinear dynamical systems. However, this has not yet occurred for parametric bilinear systems. In this work, we aim to close this gap by providing a natural extension of interpolatory projections to model reduction of parametric bilinear dynamical systems. We introduce necessary conditions that the projection subspaces must satisfy to obtain parametric tangential interpolation of each subsystem transfer function. These conditions also guarantee that the parameter sensitivities (Jacobian) of each subsystem transfer function are matched tangentially by those of the corresponding reduced-order model transfer function. Similarly, we obtain conditions for interpolating the parameter Hessian of the transfer function by including additional vectors in the projection subspaces. As in the parametric linear case, the basis construction for two-sided projections does not require computing the Jacobian or the Hessian.     

Towards hybrid system modeling of uncertain complex dynamical systems

In this paper we consider the problem of finding a low dimensional approximate model for a discrete time Markov process. This problem is of particular interest in systems that exhibit so-called metastable behavior, i.e. systems whose behavior is principally concentrated on a finite number of disjoint components of the state space. The approach developed here is based on a proper orthogonal decomposition and, unlike most existing approaches, does not require the Markov chain to be reversible. An example is presented to illustrate the effectiveness of the proposed method.     

Model reduction of nonlinear systems

The paper is concerned with the model reduction of nonlinear systems. Such methods are required in all branches of engineering. Often the level of detail in system models can cloud the essential behaviour and hence, methods are required to identify the behaviour of interest to the designer. In this contribution, the particular focus is on the empirical balanced truncation method of model reduction. A standard test case will illustrate the efficacies of the suggested approach. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

The Morse–Witten complex via dynamical systems

Given a smooth closed manifold M, the Morse–Witten complex associated to a Morse function f and a Riemannian metric g on M consists of chain groups generated by the critical points of f and a boundary operator counting isolated flow lines of the negative gradient flow. Its homology reproduces singular homology of M. The geometric approach presented here was developed in Weber [Der Morse–Witten Komplex, Diploma Thesis, TU Berlin, 1993] and is based on tools from hyperbolic dynamical systems. For instance, we apply the Grobman–Hartman theorem and the λ-lemma (Inclination Lemma) to analyze compactness and define gluing for the moduli space of flow lines.     

Stochastic model order reduction in randomly parametered linear dynamical systems

This study focuses on the development of reduced order models for stochastic analysis of complex large ordered linear dynamical systems with parametric uncertainties, with an aim to reduce the computational costs without compromising on the accuracy of the solution. Here, a twin approach to model order reduction is adopted. A reduction in the state space dimension is first achieved through system equivalent reduction expansion process which involves linear transformations that couple the effects of state space truncation in conjunction with normal mode approximations. These developments are subsequently extended to the stochastic case by projecting the uncertain parameters into the Hilbert subspace and obtaining a solution of the random eigenvalue problem using polynomial chaos expansion. Reduction in the stochastic dimension is achieved by retaining only the dominant stochastic modes in the basis space. The proposed developments enable building surrogate models for complex large ordered stochastically parametered dynamical systems which lead to accurate predictions at significantly reduced computational costs.     

Some characteristics of complex behavior of orbits in dynamical systems

We present a brief review of mathematical notions of complexity based on instability of orbits. We show that the complexity as a function of time may grow exponentially in chaotic situations or polynomially for systems with zero topological entropy. At the end we discuss the class of nonchaotic systems for which all orbits are stable but nevertheless behavior of orbits is complex. We introduce a new notion of complexity for such a kind of systems.     

The reduction principle in stability theory of dynamical and semidynamical systems

The extended version of this paper will be published inAnnali di Matematica Pura ed Aplicata.     

A connection-theoretic approach to reduction of second-order dynamical systems with symmetry

We deal with reduction of Lagrangian systems that are invariant under the action of the symmetry group. Unlike the bulk of the literature we do not rely on methods coming from the calculus of variations. Our method is based on the geometrical analysis of regular Lagrangian systems, where solutions of the Euler-Lagrange equations are interpreted as integral curves of the associated second-order differential equation field. In particular, we explain so-called Lagrange-Poincaré reduction [1] and Routh reduction [3] from the viewpoint of that vector field. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Model reduction techniques for sampled-data systems

An approximately balanced realization of linear finite-dimensional sampled-data systems is proposed. The theoretical support of the approximately balancing algorithm is represented by a result on the asymptotic expansions with respect to the sampling step of the sampled controllability and observability graminas. Reduced order models obtained as singular perturbational approximations of approximately balanced realizations of sampled-data systems are shown to be acceptable solutions to the sampled-data system model reduction problem in the sense that, enjoying some asymptotic properties, they come close to the exact solutions as the sampling step decreases. An example illustrates the results.     

The reduction principle for discrete dynamical and semidynamical systems in metric spaces

The main result of this paper is on decoupling by means of a topological transformation of a homeomorphism which admits invariant sets. A theorem of conjugacy of a given possible noninvertible mapping, which admits an invariant set, to a simpler mapping than the given one in terms of decoupling is also proved. The results are valid in an arbitrary complete metric space.This work has been completed with the financial support of Latvian Council of Science under Grants 90.224 and 93.809.     

Fuzzy dynamical systems

A fuzzy dynamical system on an underlying complete, locally compact metric state space X is defined axiomatically in terms of a fuzzy attainability set mapping on X. This definition includes as special cases crisp single and multivalued dynamical systems on X. It is shown that the support of such a fuzzy dynamical system on X is a crisp multivalued dynamical system on X, and that such a fuzzy dynamical system can be considered as a crisp dynamical system on a state space of nonempty compact fuzzy subsets of X. In addition fuzzy trajectories are defined, their existence established and various properties investigated.