LEADER 07392nam 22012135 450 001 9910437642603321 005 20231205190628.0 010 $a3-11-049775-1 010 $a3-11-049900-2 024 7 $a10.1515/9783110499001 035 $a(CKB)5450000000014730 035 $a(DE-B1597)470565 035 $a(DE-B1597)9783110499001 035 $a(OCoLC)1229161029 035 $a(MiAaPQ)EBC6637667 035 $a(Au-PeEL)EBL6637667 035 $a(OCoLC)1273982027 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/63674 035 $a(EXLCZ)995450000000014730 100 $a20210106h20202021 fg 101 0 $aeng 135 $aur||#|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aModel Order Reduction$hVolume 3$iApplications /$fPeter Benner, Wil Schilders, Stefano Grivet-Talocia, Alfio Quarteroni, Gianluigi Rozza, Luís Miguel Silveira 210 $aBerlin/Boston$cDe Gruyter$d2020 210 1$aBerlin ;$aBoston :$cDe Gruyter,$d[2020] 210 4$d©2021 215 $a1 online resource (VIII, 466 p.) 225 0 $aModel Order Reduction ;$vVolume 3 311 $a3-11-050044-2 327 $tFrontmatter --$tPreface to the third volume of Model Order Reduction --$tContents --$t1 Model reduction in chemical process optimization --$t2 Model order reduction in mechanical engineering --$t3 Case studies of model order reduction for acoustics and vibrations --$t4 Model order reduction in microelectronics --$t5 Complexity reduction of electromagnetic systems --$t6 Model reduction in computational aerodynamics --$t7 Model order reduction in neuroscience --$t8 Reduced-order modeling for applications to the cardiovascular system --$t9 From the POD-Galerkin method to sparse manifold models --$t10 Model order reduction in uncertainty quantification --$t11 Reduced-order modeling of large-scale network systems --$t12 Model order reduction and digital twins --$t13 MOR software --$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 three-volume handbook covers methods as well as applications. 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