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- W4317583540 abstract "In this paper, multi-fidelity sparse polynomial chaos expansion (SPCE), kriging as well as a combination of the two surrogate approaches into a multi-fidelity SPCE-Kriging model are constructed for inexpensive uncertainty quantification. Analytical benchmark problems are used to demonstrate the promise of these approaches. Overall, multi-fidelity models yield more accurate estimates of statistics of interest compared to using high-fidelity data alone especially at the beginning of a simulation when only a handful of high-fidelity training points are available. The SPCEs tend to be a factor of two or so better than the full-order PCE counterparts and the combination of the two approaches into the SPCE-Kriging model leads to a more accurate and flexible method overall." @default.
- W4317583540 created "2023-01-21" @default.
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- W4317583540 date "2023-01-19" @default.
- W4317583540 modified "2023-09-28" @default.
- W4317583540 title "Multi-Fidelity Kriging and Sparse Polynomial Chaos Surrogate Models for Uncertainty Quantification" @default.
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- W4317583540 doi "https://doi.org/10.2514/6.2023-0915" @default.
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