LEADER 04010nam 22006975 450 001 9911015966303321 005 20250723130301.0 010 $a3-031-87572-9 024 7 $a10.1007/978-3-031-87572-4 035 $a(CKB)39713468000041 035 $a(DE-He213)978-3-031-87572-4 035 $a(MiAaPQ)EBC32260854 035 $a(Au-PeEL)EBL32260854 035 $a(OCoLC)1535964046 035 $a(EXLCZ)9939713468000041 100 $a20250723d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA Gentle Introduction to Data, Learning, and Model Order Reduction $eTechniques and Twinning Methodologies /$fby Francisco Chinesta, Elías Cueto, Victor Champaney, Chady Ghnatios, Amine Ammar, Nicolas Hascoët, David González, Icíar Alfaro, Daniele Di Lorenzo, Angelo Pasquale, Dominique Baillargeat 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XVI, 227 p. 33 illus., 29 illus. in color.) 225 1 $aStudies in Big Data,$x2197-6511 ;$v174 311 08$a3-031-87571-0 327 $aAbstract -- Extended summary -- Part 1.Around Data -- Part 2.Around Learning -- Part 3. Around Reduction -- Part 4. Around Data Assimilation & Twinning. 330 $aThis open access book explores the latest advancements in simulation performance, driven by model order reduction, informed and augmented machine learning technologies and their combination into the so-called hybrid digital twins. It provides a comprehensive review of three key frameworks shaping modern engineering simulations: physics-based models, data-driven approaches, and hybrid techniques that integrate both. The book examines the limitations of traditional models, the role of data acquisition in uncovering underlying patterns, and how physics-informed and augmented learning techniques contribute to the development of digital twins. Organized into four sections?Around Data, Around Learning, Around Reduction, and Around Data Assimilation & Twinning?this book offers an essential resource for researchers, engineers, and students seeking to understand and apply cutting-edge simulation methodologies. 410 0$aStudies in Big Data,$x2197-6511 ;$v174 606 $aComputational intelligence 606 $aMathematics$xData processing 606 $aMachine learning 606 $aComputational Intelligence 606 $aComputational Science and Engineering 606 $aMachine Learning 615 0$aComputational intelligence. 615 0$aMathematics$xData processing. 615 0$aMachine learning. 615 14$aComputational Intelligence. 615 24$aComputational Science and Engineering. 615 24$aMachine Learning. 676 $a006.3 700 $aChinesta$b Francisco$4aut$4http://id.loc.gov/vocabulary/relators/aut$0720584 702 $aCueto$b Eli?as$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aChampaney$b Victor$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGhnatios$b Chady$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aAmmar$b Amine$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHascoët$b Nicolas$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGonza?lez$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aAlfaro$b Icíar$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aDi Lorenzo$b Daniele$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPasquale$b Angelo$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aBaillargeat$b Dominique$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911015966303321 996 $aA Gentle Introduction to Data, Learning, and Model Order Reduction$94412230 997 $aUNINA