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Titlebook: Dimension Reduction of Large-Scale Systems; Proceedings of a Wor Peter Benner,Danny C. Sorensen,Volker Mehrmann Conference proceedings 2005

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書(shū)目名稱Dimension Reduction of Large-Scale Systems
副標(biāo)題Proceedings of a Wor
編輯Peter Benner,Danny C. Sorensen,Volker Mehrmann
視頻videohttp://file.papertrans.cn/281/280456/280456.mp4
概述Includes supplementary material:
叢書(shū)名稱Lecture Notes in Computational Science and Engineering
圖書(shū)封面Titlebook: Dimension Reduction of Large-Scale Systems; Proceedings of a Wor Peter Benner,Danny C. Sorensen,Volker Mehrmann Conference proceedings 2005
描述.In the past decades, model reduction has become an ubiquitous tool in analysis and simulation of dynamical systems, control design, circuit simulation, structural dynamics, CFD, and many other disciplines dealing with complex physical models. The aim of this book is to survey some of the most successful model reduction methods in tutorial style articles and to present benchmark problems from several application areas for testing and comparing existing and new algorithms. As the discussed methods have often been developed in parallel in disconnected application areas, the intention of the mini-workshop in Oberwolfach and its proceedings is to make these ideas available to researchers and practitioners from all these different disciplines..
出版日期Conference proceedings 2005
關(guān)鍵詞circuit simulation; control design; dynamical systems; model; model reduction; numerical methods; simulati
版次1
doihttps://doi.org/10.1007/3-540-27909-1
isbn_softcover978-3-540-24545-2
isbn_ebook978-3-540-27909-9Series ISSN 1439-7358 Series E-ISSN 2197-7100
issn_series 1439-7358
copyrightSpringer-Verlag Berlin Heidelberg 2005
The information of publication is updating

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978-3-540-24545-2Springer-Verlag Berlin Heidelberg 2005
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Dimension Reduction of Large-Scale Systems978-3-540-27909-9Series ISSN 1439-7358 Series E-ISSN 2197-7100
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Sozialpolitische Strategien gegen ArmutA benchmark for the heat transfer problem, related to modeling of a microhotplate gas sensor, is presented. It can be used to apply model reduction algorithms to a linear first-order problem as well as when an input function is nonlinear.
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