Reduction of large dynamical systems by minimization of evolution rate
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Reduction of large dynamical systems by minimization of evolution rate

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Published by National Aeronautics and Space Administration, Langley Research Center, National Technical Information Service, distributor in Hampton, Va, [Springfield, Va .
Written in English


  • Dynamical systems.,
  • Turbulent combustion.,
  • Reaction kinetics.

Book details:

Edition Notes

StatementSharath S. Girimaji.
SeriesICASE report -- no. 99-15., [NASA contractor report] -- NASA/CR-1999-209121., NASA contractor report -- NASA CR-209121.
ContributionsLangley Research Center.
The Physical Object
Pagination1 v.
ID Numbers
Open LibraryOL15559360M

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Get this from a library! Reduction of large dynamical systems by minimization of evolution rate. [Sharath S Girimaji; Langley Research Center.]. The mathematician interested in mathematical biology will find this book useful. It may be used as a supplementary textbook for graduate topics related to applications of dynamical systems on mathematical biology. The book includes an impressive list of references.” (George Karakostas, zbMATH , )Cited by: A must-have book for any one working on model order reduction or dealing with large scale dynamical system. By reading this book I clearly understood the concept of reachability and observability and how it is applied to better understand a dynamical system. Overall a book with excellent source of knowledge, both as text and reference book.5/5(1). The optimal model reduction of linear dynamical systems in the H 2 norm via the iterative rational Krylov algorithm (IRKA) [29] has been generalized to bilinear systems via bilinear IRKA (B .

This is the internet version of Invitation to Dynamical Systems. Unfortunately, the original publisher has let this book go out of print. The version you are now reading is pretty close to the original version (some formatting has changed, so page numbers are unlikely to be the same, and the fonts are different). Model Reduction for Linear Dynamical Systems Peter Benner Max Planck Institute for Dynamics of Complex Technical Systems Computational Methods in Systems and Control Theory combined with Guyan reduction (static condensation) Craig-Bampton method. Max Planck Institute Magdeburg Peter Benner, MOR for Linear Dynamical Systems 6/52 File Size: 3MB. Large dynamical systems also arise from circuit simulation; e.g., [1]. Often numerical methods for controller design or simulation cannot be applied to very large systems because of their extensive numerical costs. This motivates model reduction, which is the approximation of the original, large realization by a realization of smaller order. meaning. Dynamical systems arise in the study of fluid flow, population genetics, ecology, and many other diverse fields where one seeks to model the change in behavior of a system over time. Several of the global features of dynamical systems such as File Size: KB.

The systems considered include those with transfer or rate processes that occur in a finite time and in equipment of finite dimensions. These processes include heat and separation operations, which are found in heat and mass exchangers, thermal networks, energy convertors, energy recovery units, storage systems, chemical reactors, and chemical. We study the convergence properties of an alternating proximal minimization algorithm for nonconvex structured functions of the type: L(x,y)=f(x)+Q(x,y)+g(y), where f and g are proper lower semicontinuous functions, defined on Euclidean spaces, and Q is a smooth function that couples the variables x and algorithm can be viewed as a proximal regularization of the usual Gauss-Seidel method. An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Identifying accurate and yet interpretable low-order models from data has gained a renewed interest over the past decade. In the present work, we illustrate how the combined use of dimensionality reduction and sparse system identification techniques allows us to obtain an accurate model of the chaotic thermal convection in a two-dimensional annular thermosyphon. Taking as guidelines the.