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Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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...

Deskribapen osoa

Gorde:
Egile Nagusiak: Fritzen, Felix, Ryckelynck, David
Formatua: Online
Hizkuntza:ingelesa
Argitaratua: MDPI - Multidisciplinary Digital Publishing Institute 2021
Gaiak:
TA1-2040
T1-995
supervised machine learning
proper orthogonal decomposition (POD)
PGD compression
stabilization
nonlinear reduced order model
gappy POD
symplectic model order reduction
neural network
snapshot proper orthogonal decomposition
3D reconstruction
microstructure property linkage
nonlinear material behaviour
proper orthogonal decomposition
reduced basis
ECSW
geometric nonlinearity
POD
model order reduction
elasto-viscoplasticity
sampling
surrogate modeling
model reduction
enhanced POD
archive
modal analysis
low-rank approximation
computational homogenization
artificial neural networks
unsupervised machine learning
large strain
reduced-order model
proper generalised decomposition (PGD)
a priori enrichment
elastoviscoplastic behavior
error indicator
computational homogenisation
empirical cubature method
nonlinear structural mechanics
reduced integration domain
model order reduction (MOR)
structure preservation of symplecticity
heterogeneous data
reduced order modeling (ROM)
parameter-dependent model
data science
Hencky strain
dynamic extrapolation
tensor-train decomposition
hyper-reduction
empirical cubature
randomised SVD
machine learning
inverse problem plasticity
proper symplectic decomposition (PSD)
finite deformation
Hamiltonian system
DEIM
GNAT
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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