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Space and chaos‐expansion Galerkin proper orthogonal decomposition low‐order discretization of partial differential equations for uncertainty quantification
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EFFICIENT APPROXIMATION OF HIGH-DIMENSIONAL EXPONENTIALS BY TENSOR NETWORKS
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format
Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck and Robert Scheichl SIAM/ASA Journal on Uncertainty Quantification 10(3) 1191 (2022) https://doi.org/10.1137/20M1314653
A Tensor Splitting AOR Iterative Method for Solving a Tensor Absolute Value Equation
Low‐rank solution of an optimal control problem constrained by random Navier‐Stokes equations
Peter Benner, Sergey Dolgov, Akwum Onwunta and Martin Stoll International Journal for Numerical Methods in Fluids 92(11) 1653 (2020) https://doi.org/10.1002/fld.4843
Parallel cross interpolation for high-precision calculation of high-dimensional integrals
Tucker Tensor Analysis of Matérn Functions in Spatial Statistics
Alexander Litvinenko, David Keyes, Venera Khoromskaia, Boris N. Khoromskij and Hermann G. Matthies Computational Methods in Applied Mathematics 19(1) 101 (2019) https://doi.org/10.1515/cmam-2018-0022
Direct tensor-product solution of one-dimensional elliptic equations with parameter-dependent coefficients
Model Validation and Uncertainty Quantification, Volume 3
Jinchun Lan and Zhike Peng Conference Proceedings of the Society for Experimental Mechanics Series, Model Validation and Uncertainty Quantification, Volume 3 73 (2016) https://doi.org/10.1007/978-3-319-29754-5_7
Curves and Surfaces
Angelos Mantzaflaris, Bert Jüttler, B. N. Khoromskij and Ulrich Langer Lecture Notes in Computer Science, Curves and Surfaces 9213 321 (2015) https://doi.org/10.1007/978-3-319-22804-4_24
Tensor numerical methods in quantum chemistry: from Hartree–Fock to excitation energies