Data-driven polynomial chaos expansions for characterization of complex fluid rheology: Case study of phosphate slurry
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DOI: 10.1016/j.ress.2021.107923
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- Cooper, James & Lombardi, Rachel & Boardman, David & Carliell-Marquet, Cynthia, 2011. "The future distribution and production of global phosphate rock reserves," Resources, Conservation & Recycling, Elsevier, vol. 57(C), pages 78-86.
- Dubreuil, S. & Berveiller, M. & Petitjean, F. & Salaün, M., 2014. "Construction of bootstrap confidence intervals on sensitivity indices computed by polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 263-275.
- Elmaz, Furkan & Yücel, Özgün & Mutlu, Ali Yener, 2020. "Predictive modeling of biomass gasification with machine learning-based regression methods," Energy, Elsevier, vol. 191(C).
- Oladyshkin, Sergey & Nowak, Wolfgang, 2018. "Incomplete statistical information limits the utility of high-order polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 137-148.
- Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
- Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
- Crestaux, Thierry & Le Maıˆtre, Olivier & Martinez, Jean-Marc, 2009. "Polynomial chaos expansion for sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1161-1172.
- Chengcheng Tao & Barbara G. Kutchko & Eilis Rosenbaum & Mehrdad Massoudi, 2020. "A Review of Rheological Modeling of Cement Slurry in Oil Well Applications," Energies, MDPI, vol. 13(3), pages 1-55, January.
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- Yao, Wen & Zheng, Xiaohu & Zhang, Jun & Wang, Ning & Tang, Guijian, 2023. "Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
- Zheng, Xiaohu & Yao, Wen & Zhang, Xiaoya & Qian, Weiqi & Zhang, Hairui, 2023. "Parameterized coefficient fine-tuning-based polynomial chaos expansion method for sphere-biconic reentry vehicle reliability analysis and design," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
- Wang, Zhenqiang & Jia, Gaofeng, 2023. "Extended sample-based approach for efficient sensitivity analysis of group of random variables," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
- Thapa, Mishal & Missoum, Samy, 2022. "Uncertainty quantification and global sensitivity analysis of composite wind turbine blades," Reliability Engineering and System Safety, Elsevier, vol. 222(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).
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Keywords
Data-driven modeling; Polynomial chaos expansion; Rheology; Bagging; Phosphate slurry; Machine learning; Sensitivity analysis;All these keywords.
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