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Palabras clave – provistas por la editorial

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