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100			_aFritzen, Felix||Ryckelynck, David
245		0	_aMachine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics
546			_aEnglish[eng]
650			_asupervised 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
856			_uhttps://mdpi.com/books/pdfview/book/1551
942			_cEB
999			_c23745 _d23745