LEADER 04029nam 22007095 450 001 9910299759903321 005 20200702175640.0 010 $a3-7091-1794-1 024 7 $a10.1007/978-3-7091-1794-1 035 $a(CKB)3710000000228771 035 $a(EBL)1966782 035 $a(OCoLC)891384847 035 $a(SSID)ssj0001354006 035 $a(PQKBManifestationID)11773471 035 $a(PQKBTitleCode)TC0001354006 035 $a(PQKBWorkID)11322489 035 $a(PQKB)11244055 035 $a(MiAaPQ)EBC1966782 035 $a(DE-He213)978-3-7091-1794-1 035 $a(PPN)181353598 035 $a(EXLCZ)993710000000228771 100 $a20140902d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSeparated Representations and PGD-Based Model Reduction$b[electronic resource] $eFundamentals and Applications /$fedited by Francisco Chinesta, Pierre Ladevèze 205 $a1st ed. 2014. 210 1$aVienna :$cSpringer Vienna :$cImprint: Springer,$d2014. 215 $a1 online resource (234 p.) 225 1 $aCISM International Centre for Mechanical Sciences, Courses and Lectures,$x0254-1971 ;$v554 300 $aDescription based upon print version of record. 311 $a3-7091-1793-3 320 $aIncludes bibliographical references. 327 $aFrom the Contents: Model order reduction based on proper orthogonal decomposition: Model reduction: extracting relevant information -- Interpolation of reduced basis: a geometrical approach -- POD for non-linear models. 330 $aThe papers in this volume start with a description of  the construction of reduced models through a review of Proper Orthogonal Decomposition (POD) and reduced basis models, including their mathematical foundations and some challenging applications, then followed by a description of a  new generation of simulation strategies based on the use of separated representations (space-parameters, space-time, space-time-parameters, space-space,?), which have led to what is known as Proper Generalized Decomposition (PGD) techniques. The models can be enriched by treating parameters as additional coordinates, leading to fast and inexpensive online calculations based on richer offline parametric solutions. Separated representations are analyzed in detail in the course, from their mathematical foundations to their most spectacular applications. It is also shown how such an approximation could evolve into a new paradigm in computational science, enabling one to circumvent various computational issues in a vast array of applications in engineering science. 410 0$aCISM International Centre for Mechanical Sciences, Courses and Lectures,$x0254-1971 ;$v554 606 $aMechanics 606 $aMechanics, Applied 606 $aComputer mathematics 606 $aComputer-aided engineering 606 $aTheoretical and Applied Mechanics$3https://scigraph.springernature.com/ontologies/product-market-codes/T15001 606 $aComputational Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/M14026 606 $aComputer-Aided Engineering (CAD, CAE) and Design$3https://scigraph.springernature.com/ontologies/product-market-codes/I23044 615 0$aMechanics. 615 0$aMechanics, Applied. 615 0$aComputer mathematics. 615 0$aComputer-aided engineering. 615 14$aTheoretical and Applied Mechanics. 615 24$aComputational Science and Engineering. 615 24$aComputer-Aided Engineering (CAD, CAE) and Design. 676 $a004 676 $a620 676 $a620.00420285 676 $a620.1 702 $aChinesta$b Francisco$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLadevèze$b Pierre$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910299759903321 996 $aSeparated Representations and PGD-Based Model Reduction$92204606 997 $aUNINA