Model order reduction based on proper generalized decomposition for the propagation of uncertainties in structural dynamics

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A multiscale method with patch for the solution of stochastic partial differential equations with localized uncertainties (2013, background): “Here the classical canonical decompositions and the hierarchical canonical decompositions as in [26, 27] are introduced, the latter ones giving very low ranks representations.”
POD-DEIM model order reduction for strain softening viscoplasticity (2017, methodology): “In the field of solid mechanics, MOR techniques such as POD-G [12,13], Karhunen Loeve Expansion [14,15], and Proper Generalized Decomposition (PGD) [16,17] are employed to reduce the dimension of the system of equations.”
Low-rank methods for high-dimensional approximation and model order reduction (2015, unknown): “nt model, so that parameter-independent equations have the structure of standard problems which can be solved with available solution codes for parameter-independent models. The reader is referred to [63, 64, 66, 20] for practical implementations and illustrations of the behavior of these algorithms”
Computational Reduction for Parametrized PDEs: Strategies and Applications (2012, methodology): “Two additional techniques – indeed quite close to POD – for generating reduced spaces are the Centroidal Voronoi Tessellation [7–9] and the Proper Generalized Decomposition [10,11,16,36].”