TY  - GEN
AU  - Fritzen,Felix
AU  - Ryckelynck,David
TI  - Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics
SN  - books978-3-03921-410-5
PY  - 2019///
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - supervised machine learning
KW  - proper orthogonal decomposition (POD)
KW  - PGD compression
KW  - stabilization
KW  - nonlinear reduced order model
KW  - gappy POD
KW  - symplectic model order reduction
KW  - neural network
KW  - snapshot proper orthogonal decomposition
KW  - 3D reconstruction
KW  - microstructure property linkage
KW  - nonlinear material behaviour
KW  - proper orthogonal decomposition
KW  - reduced basis
KW  - ECSW
KW  - geometric nonlinearity
KW  - POD
KW  - model order reduction
KW  - elasto-viscoplasticity
KW  - sampling
KW  - surrogate modeling
KW  - model reduction
KW  - enhanced POD
KW  - archive
KW  - modal analysis
KW  - low-rank approximation
KW  - computational homogenization
KW  - artificial neural networks
KW  - unsupervised machine learning
KW  - large strain
KW  - reduced-order model
KW  - proper generalised decomposition (PGD)
KW  - a priori enrichment
KW  - elastoviscoplastic behavior
KW  - error indicator
KW  - computational homogenisation
KW  - empirical cubature method
KW  - nonlinear structural mechanics
KW  - reduced integration domain
KW  - model order reduction (MOR)
KW  - structure preservation of symplecticity
KW  - heterogeneous data
KW  - reduced order modeling (ROM)
KW  - parameter-dependent model
KW  - data science
KW  - Hencky strain
KW  - dynamic extrapolation
KW  - tensor-train decomposition
KW  - hyper-reduction
KW  - empirical cubature
KW  - randomised SVD
KW  - machine learning
KW  - inverse problem plasticity
KW  - proper symplectic decomposition (PSD)
KW  - finite deformation
KW  - Hamiltonian system
KW  - DEIM
KW  - GNAT
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/1551
UR  - https://directory.doabooks.org/handle/20.500.12854/52520
ER  -