3 edition of **Reduction of large dynamical systems by minimization of evolution rate** found in the catalog.

Reduction of large dynamical systems by minimization of evolution rate

- 76 Want to read
- 34 Currently reading

Published
**1999**
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.

**Edition Notes**

Statement | Sharath S. Girimaji. |

Series | ICASE report -- no. 99-15., [NASA contractor report] -- NASA/CR-1999-209121., NASA contractor report -- NASA CR-209121. |

Contributions | Langley Research Center. |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 1 v. |

ID Numbers | |

Open Library | OL15559360M |

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Reduction of Large Dynamical Systems by Minimization of Evolution Rate Article (PDF Available) in Physical Review Letters 82(11) June with 27 Reads How we measure 'reads'Author: Sharath Girimaji. EVOLUTION RATE SHARATH S. GIRIMAJI* Abstract.

Reduction of a large system of equations to a lower-dimensional system of similar dynamics is investigated. For dynamical systems with disparate timescales, a criterion for determining redundant dimensions and a general reduction method based on the minimization of evolution rate are by: 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.

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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.

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The techniques uses dynamical filters and classifiers optimized for a particular category of signals of interest. The dynamical filters and classifiers can be implemented using models based on delayed. Batista L, Bastogne T and Djermoune E Identification of dynamical biological systems based on mixed-effect models Proceedings of the 31st Annual ACM Symposium on Applied Computing, () Nõmm S and Moog C () Further results on identifiability of discrete-time nonlinear systems, Automatica (Journal of IFAC), C, (), Online.Tuhin Sahai and José Miguel Pasini, Uncertainty quantification in hybrid dynamical systems, Journal of Computational Physics,(), ().

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