04408nam 2200997z- 450 991036775940332120231214133220.03-03921-410-1(CKB)4100000010106123(oapen)https://directory.doabooks.org/handle/20.500.12854/52520(EXLCZ)99410000001010612320202102d2019 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational MechanicsMDPI - Multidisciplinary Digital Publishing Institute20191 electronic resource (254 p.)3-03921-409-8 The 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.supervised machine learningproper orthogonal decomposition (POD)PGD compressionstabilizationnonlinear reduced order modelgappy PODsymplectic model order reductionneural networksnapshot proper orthogonal decomposition3D reconstructionmicrostructure property linkagenonlinear material behaviourproper orthogonal decompositionreduced basisECSWgeometric nonlinearityPODmodel order reductionelasto-viscoplasticitysamplingsurrogate modelingmodel reductionenhanced PODarchivemodal analysislow-rank approximationcomputational homogenizationartificial neural networksunsupervised machine learninglarge strainreduced-order modelproper generalised decomposition (PGD)a priori enrichmentelastoviscoplastic behaviorerror indicatorcomputational homogenisationempirical cubature methodnonlinear structural mechanicsreduced integration domainmodel order reduction (MOR)structure preservation of symplecticityheterogeneous datareduced order modeling (ROM)parameter-dependent modeldata scienceHencky straindynamic extrapolationtensor-train decompositionhyper-reductionempirical cubaturerandomised SVDmachine learninginverse problem plasticityproper symplectic decomposition (PSD)finite deformationHamiltonian systemDEIMGNATFritzen Felixauth1322425Ryckelynck DavidauthBOOK9910367759403321Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics3034984UNINA