Fritzen, Felix||Ryckelynck, David <br/><br/>
Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics 

<br/><br/><br/>English[eng] 
<br/><br/><label>Subjects--Topical Terms: </label><br/>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