Results 291 to 300 of about 4,793,633 (336)

Symbolic model order reduction

Proceedings of the 2003 IEEE International Workshop on Behavioral Modeling and Simulation, 2004
Symbolic model order reduction (SMOR) is the problem of reducing a large circuit that contains symbolic circuit parameters to smaller low order models at its ports. Several methods, including symbol isolation, single frequency point reduction, and multiple frequency point reduction, are described and compared.
B.P. Hu, G. Shi, C.-J.R. Shi
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Multilevel model order reduction

IEEE Microwave and Wireless Components Letters, 2004
We present a multilevel Model Order Reduction scheme for enhancing numerical analysis of electromagnetic fields by means of grid based techniques. The scheme allows one to create nested macromodels and combine macromodels with the Fast Frequency Sweep.
L. Kulas, M. Mrozowski
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Model order reduction by selective sensitivity

AIAA Journal, 1997
Summary: Many industrial structures are represented by models with a large number of degrees of freedom, thus making their use complex and costly. Model order reduction alleviates this problem by elaborating lower-dimensional models that satisfy some properties of the refined model.
Cogan, Scott, Lallement, Gérard, Ayer, Frédérique, Ben-Haim, Yakov   +3 more
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Model Order Reduction using fractional order systems

2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2016
In this work, a Model Order Reduction (MOR) technique is proposed to reduce the number of parameters required to describe a high dimensional integer system. Motivated by the fact a fractional order model is able to describe a large amount of system dynamics, the order reduction is achieved by expressing a given system as a product of fixed unknown ...
Mohamed Taha, Dia Abualnadi, Omar Hasan
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Interpolatory Model Order Reduction Method for Second Order Systems

Asian Journal of Control, 2017
AbstractIn this paper, we propose a structure‐preserving model reduction method for second‐order systems based onH2optimal interpolation. In the iterative process of the proposed method, an algorithm is presented for selecting interpolation points in order to control the dimension of the reduced system.
Zhi‐Yong Qiu, Yao‐Lin Jiang, Jia‐Wei Yuan   +2 more
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TLM Model Order Reduction

2004
The Finite-Difference Time-Domain (FDTD) method, Finite-Integration Technique (FIT) and the Transmission Line Matrix (TLM) method provide for discrete approximations of electromagnetic boundary value problems cast in state-space forms. The dimension of the generated state-space models is usually very large.
Dzianis Lukashevich, Peter Russer, Andreas Cangellaris   +2 more
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Introduction to Model Order Reduction

2008
In this first section we present a high level discussion on computational science, and the need for compact models of phenomena observed in nature and industry. We argue that much more complex problems can be addressed by making use of current computing technology and advanced algorithms, but that there is a need for model order reduction in order to ...
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Advanced Topics in Model Order Reduction

2015
This chapter contains three advanced topics in model order reduction (MOR): nonlinear MOR, MOR for multi-terminals (or multi-ports) and finally an application in deriving a nonlinear macromodel covering phase shift when coupling oscillators. The sections are offered in a preferred order for reading, but can be read independently.
Harutyunyan, Davit, Ionutiu, Roxana, ter Maten, E. Jan W., Rommes, Joost, Schilders, Wilhelmus H. A., Striebel, Michael   +5 more
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Parameter-dependent model order reduction

International Journal of Control, 1997
L In this paper we consider the optimal model reduction problem where the plant 2 model depends on parameters that are measurable. Such cases occur in many on-line as well as off-line applications and the question that arises is how to update the reduced order model without complete re-solution of the problem.
Y. Halevi, A. Zlochevsky, T. Gilat
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On symbolic model order reduction

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2006
Symbolic model order reduction (SMOR) is a macromodeling technique that generates reduced-order models while retaining the parameters in the original models. Such symbolic reduced-order models can be repeatedly simulated with a greater efficiency for varying model parameters.
null Guoyong Shi, null Bo Hu, C.-J.R. Shi   +2 more
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