LEADER 01452nam--2200397---450- 001 990003159000203316 005 20081013105305.0 010 $a978-88-348-8284-9 035 $a000315900 035 $aUSA01000315900 035 $a(ALEPH)000315900USA01 035 $a000315900 100 $a20081013d2008----km-y0itay50------ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aGestione delle risorse e finanziamento degli enti locali territoriali$eatti dellagiornata di studio svoltasi a Sassari il 13 ottobre 2006$fa cura di Valerio Ficari, Lucia Giovanelli e Giuliana G. Carboni 210 $aTorino$cGiappichelli$d2008 215 $aIX, 208 p.$d24 cm 225 2 $aDipartimento di economia impresa regolamentazione$fUniversità degli studi di Sassari$iStudi e Ricerche$v9 410 0$12001$aDipartimento di economia impresa regolamentazione$v9 454 1$12001 461 1$1001-------$12001 606 0 $aEnti locali$xGestione finanziaria$2BNCF 676 $a352.439 700 1$aFICARI,$bValerio$0284905 701 1$aGIOVANELLI,$bLucia$0116242 702 1$aCARBONI,$bGiuliana G. 801 0$aIT$bsalbc$gISBD 912 $a990003159000203316 951 $a352.439 GES 1$b61179 G.$c352.439 GES$d00167773 959 $aBK 969 $aECO 979 $aFIORELLA$b90$c20081013$lUSA01$h1053 996 $aGestione delle risorse e finanziamento degli enti locali territoriali$91017833 997 $aUNISA LEADER 02350oam 2200601I 450 001 9910704399103321 005 20170816114731.0 035 $a(CKB)5470000002439689 035 $a(OCoLC)778057800 035 $a(EXLCZ)995470000002439689 100 $a20120223j199611 ua 0 101 0 $aeng 135 $aurbn||||a|a|| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProgressive Damage Analysis of Laminated Composite (PDALC) $ea computational model implemented in the NASA COMET finite element code /$fDavid C. Lo [and three others] 210 1$aHampton, Virginia :$cNational Aeronautics and Space Administration, Langley Research Center,$dNovember 1996. 215 $a1 online resource (41 pages) $cillustrations 225 1 $aNASA technical memorandum ;$v4724 300 $aTitle from title screen (viewed on Feb. 23, 2016). 300 $a"November 1996." 300 $a"Performing organization: NASA Langley Research Center, Hampton, VA"--Report documentation page. 320 $aIncludes bibliographical references (page 32). 517 $aProgressive Damage Analysis of Laminated Composite 606 $aLaminated materials$xCracking$xMathematical models 606 $aComposite materials$xCracking$xMathematical models 606 $aFinite element method$xData processing 606 $aResidual strength$2nasat 606 $aStructural analysis$2nasat 606 $aMatrix materials$2nasat 606 $aCracking (fracturing)$2nasat 606 $aDamage assessment$2nasat 615 0$aLaminated materials$xCracking$xMathematical models. 615 0$aComposite materials$xCracking$xMathematical models. 615 0$aFinite element method$xData processing. 615 7$aResidual strength. 615 7$aStructural analysis. 615 7$aMatrix materials. 615 7$aCracking (fracturing) 615 7$aDamage assessment. 700 $aLo$b David C.$01419697 712 02$aLangley Research Center, 712 02$aUnited States.$bNational Aeronautics and Space Administration, 801 0$bOCLCE 801 1$bOCLCE 801 2$bOCLCQ 801 2$bOCLCO 801 2$bGPO 906 $aBOOK 912 $a9910704399103321 996 $aProgressive Damage Analysis of Laminated Composite (PDALC)$93535091 997 $aUNINA LEADER 03888nam 22006975 450 001 9910741194703321 005 20260330180748.0 010 $a9783031316364 010 $a3031316363 024 7 $a10.1007/978-3-031-31636-4 035 $a(CKB)27965612600041 035 $a(DE-He213)978-3-031-31636-4 035 $a(PPN)272272647 035 $a(MiAaPQ)EBC30682611 035 $a(Au-PeEL)EBL30682611 035 $a(MiAaPQ)EBC30766880 035 $a(Au-PeEL)EBL30766880 035 $a(EXLCZ)9927965612600041 100 $a20230809d2023 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Driven Model Learning for Engineers $eWith Applications to Univariate Time Series /$fby Guillaume Mercère 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (X, 212 p. 93 illus., 54 illus. in color.) 311 08$a9783031316357 330 $aThe main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail. As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. 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