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- W2110632054 endingPage "55" @default.
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- W2110632054 abstract "This work proposes a sampling-based (non-intrusive) approach within the context of low-rank separated representations to tackle the issue of curse-of-dimensionality associated with the solution of models, e.g., PDEs/ODEs, with high-dimensional random inputs. Under some conditions discussed in details, the number of random realizations of the solution, required for a successful approximation, grows linearly with respect to the number of random inputs. The construction of the separated representation is achieved via a regularized alternating least-squares regression, together with an error indicator to estimate model parameters. The computational complexity of such a construction is quadratic in the number of random inputs. The performance of the method is investigated through its application to three numerical examples including two ODE problems with high-dimensional random inputs." @default.
- W2110632054 created "2016-06-24" @default.
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- W2110632054 creator A5008793826 @default.
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- W2110632054 date "2013-08-01" @default.
- W2110632054 modified "2023-10-01" @default.
- W2110632054 title "Non-intrusive low-rank separated approximation of high-dimensional stochastic models" @default.
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- W2110632054 doi "https://doi.org/10.1016/j.cma.2013.04.003" @default.
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