Efficient dimension reduction and surrogate-based sensitivity analysis for expensive models with high-dimensional outputs
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DOI: 10.1016/j.ress.2019.106725
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Cited by:
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- WoongHee Jung & Aikaterini P. Kyprioti & Ehsan Adeli & Alexandros A. Taflanidis, 2023. "Exploring the sensitivity of probabilistic surge estimates to forecast errors," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(2), pages 1371-1409, January.
- Xiong, Qingwen & Du, Peng & Deng, Jian & Huang, Daishun & Song, Gongle & Qian, Libo & Wu, Zenghui & Luo, Yuejian, 2022. "Global sensitivity analysis for nuclear reactor LBLOCA with time-dependent outputs," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
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- Golparvar, Behzad & Papadopoulos, Petros & Ezzat, Ahmed Aziz & Wang, Ruo-Qian, 2021. "A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power," Applied Energy, Elsevier, vol. 299(C).
- Kröker, Ilja & Oladyshkin, Sergey, 2022. "Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
- López-Lopera, Andrés F. & Idier, Déborah & Rohmer, Jérémy & Bachoc, François, 2022. "Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
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Keywords
Sensitivity analysis; Kriging surrogate model; High-dimensional outputs; Dimension reduction; Principal component analysis; Sobol’ index;All these keywords.
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