LEADER 01236nam--2200373---45-- 001 990000371990203316 010 $a3-540-41456-8 035 $a0037199 035 $aUSA010037199 035 $a(ALEPH)000037199USA01 035 $a0037199 100 $a20010323d2000----km-y0ITAy0103-------ba 101 0 $aENG 102 $aDE 200 1 $aDatabase theory-ICDT 2001$e8th international conference$eLondon, UK, January 4-6, 2001$eprocedings$fJan Van den Bussche...[et al.] (eds.) 210 $aBerlin$cSpringer-Verlag$dcopyr. 2000 215 $aX, 449 p.$cill.$d20 cm. 225 2 $aLecture notes in computer science$v1973 410 $12001$aLecture notes in computer science$v1973 610 1 $aArchivi di dati$aCongressi$a2001 610 1 $aCongressi$aLondra$a2001 676 $a005.74 702 1$aVan den Bussche,$bJan 801 0$aITA$bCBS$gISBD 912 $a990000371990203316 951 $a001 LNCS (1973)$b0026047 CBS$c001$d00104583 959 $aBK 969 $aSCI 979 $aALANDI$b90$c20010323$lUSA01$h1140 979 $aALANDI$b90$c20010410$lUSA01$h1131 979 $c20020403$lUSA01$h1645 979 $aPATRY$b90$c20040406$lUSA01$h1626 996 $aDatabase theory-ICDT 2001$9877541 997 $aUNISA LEADER 02461nam 2200433 450 001 9910795157603321 005 20230803211758.0 010 $a3-8325-9163-X 035 $a(CKB)4910000000017336 035 $a(MiAaPQ)EBC5850403 035 $a(Au-PeEL)EBL5850403 035 $a(OCoLC)1112422358 035 $a5a8e86f0-e608-4bb4-8b7b-66c5b0dd2d03 035 $a(EXLCZ)994910000000017336 100 $a20190917d2014 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApproximation methods for high dimensional simulation results $eparameter sensitivity analysis and propagation of variations for process chains /$fDaniela Steffes-lai 210 1$aBerlin :$cLogos Verlag,$d[2014] 210 4$dİ2014 215 $a1 online resource (ix, 219 pages) $cillustrations 300 $aPublicationDate: 20140630 311 $a3-8325-3696-5 320 $aIncludes bibliographical references. 330 $aLong description: This work addresses the analysis of a sequential chain of processing steps, which is particularly important for the manufacture of robust product components. In each processing step, the material properties may have changed and distributions of related characteristics, for example, strains, may become inhomogeneous. For this reason, the history of the process including design-parameter uncertainties becomes relevant for subsequent processing steps. Therefore, we have developed a methodology, called PRO-CHAIN, which enables an efficient analysis, quantification, and propagation of uncertainties for complex process chains locally on the entire mesh. This innovative methodology has the objective to improve the overall forecast quality, specifically, in local regions of interest, while minimizing the computational effort of subsequent analysis steps. We have demonstrated the benefits and efficiency of the methodology proposed by means of real applications from the automotive industry. 606 $aSensitivity theory (Mathematics)$xSimulation methods 615 0$aSensitivity theory (Mathematics)$xSimulation methods. 676 $a003.5 700 $aSteffes-lai$b Daniela$01561693 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910795157603321 996 $aApproximation methods for high dimensional simulation results$93828664 997 $aUNINA