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101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aModel Order Reduction$hVolume 1$iSystem- and Data-Driven Methods and Algorithms /$fed. by Peter Benner
210  1$aBerlin ;$aBoston :$cDe Gruyter,$d[2021]
210  4$d©2021
215   $a1 online resource (X, 378 p.)
225 0 $aModel Order Reduction ;$vVolume 1
300   $aDescription based upon print version of record.
311   $a3-11-050043-4 
327   $tFrontmatter --$tPreface to the first volume of Model Order Reduction --$tContents --$t1 Model order reduction: basic concepts and notation --$t2 Balancing-related model reduction methods --$t3 Model order reduction based on moment-matching --$t4 Modal methods for reduced order modeling --$t5 Post-processing methods for passivity enforcement --$t6 The Loewner framework for system identification and reduction --$t7 Manifold interpolation --$t8 Vector fitting --$t9 Kernel methods for surrogate modeling --$t10 Kriging: methods and applications --$tIndex
330   $aAn increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques.
606   $aMATHEMATICS / Numerical Analysis$2bisacsh
615  7$aMATHEMATICS / Numerical Analysis.
676   $a515.353
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801  0$bDE-B1597
801  1$bDE-B1597
906   $aBOOK
912   $a996445849203316
996   $aModel order reduction$91503415
997   $aUNISA