LEADER 04866nam 22011293a 450 001 9910367759403321 005 20250203235433.0 010 $a9783039214105 010 $a3039214101 024 8 $a10.3390/books978-3-03921-410-5 035 $a(CKB)4100000010106123 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/52520 035 $a(ScCtBLL)523e4334-39f7-4bd6-8762-07f07acabc8e 035 $a(OCoLC)1163823825 035 $a(oapen)doab52520 035 $a(EXLCZ)994100000010106123 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics$fFelix Fritzen, David Ryckelynck 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (254 p.) 311 08$a9783039214099 311 08$a3039214098 330 $aThe use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics. 606 $aHistory of engineering and technology$2bicssc 610 $asupervised machine learning 610 $aproper orthogonal decomposition (POD) 610 $aPGD compression 610 $astabilization 610 $anonlinear reduced order model 610 $agappy POD 610 $asymplectic model order reduction 610 $aneural network 610 $asnapshot proper orthogonal decomposition 610 $a3D reconstruction 610 $amicrostructure property linkage 610 $anonlinear material behaviour 610 $aproper orthogonal decomposition 610 $areduced basis 610 $aECSW 610 $ageometric nonlinearity 610 $aPOD 610 $amodel order reduction 610 $aelasto-viscoplasticity 610 $asampling 610 $asurrogate modeling 610 $amodel reduction 610 $aenhanced POD 610 $aarchive 610 $amodal analysis 610 $alow-rank approximation 610 $acomputational homogenization 610 $aartificial neural networks 610 $aunsupervised machine learning 610 $alarge strain 610 $areduced-order model 610 $aproper generalised decomposition (PGD) 610 $aa priori enrichment 610 $aelastoviscoplastic behavior 610 $aerror indicator 610 $acomputational homogenisation 610 $aempirical cubature method 610 $anonlinear structural mechanics 610 $areduced integration domain 610 $amodel order reduction (MOR) 610 $astructure preservation of symplecticity 610 $aheterogeneous data 610 $areduced order modeling (ROM) 610 $aparameter-dependent model 610 $adata science 610 $aHencky strain 610 $adynamic extrapolation 610 $atensor-train decomposition 610 $ahyper-reduction 610 $aempirical cubature 610 $arandomised SVD 610 $amachine learning 610 $ainverse problem plasticity 610 $aproper symplectic decomposition (PSD) 610 $afinite deformation 610 $aHamiltonian system 610 $aDEIM 610 $aGNAT 615 7$aHistory of engineering and technology 700 $aFritzen$b Felix$01322425 702 $aRyckelynck$b David 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910367759403321 996 $aMachine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics$93034984 997 $aUNINA