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Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion

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Cited by:

  1. Zan Wang & Mitchell J. Small, 2016. "Statistical performance of CO 2 leakage detection using seismic travel time measurements," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 6(1), pages 55-69, February.
  2. Allaire, Douglas & Noel, George & Willcox, Karen & Cointin, Rebecca, 2014. "Uncertainty quantification of an Aviation Environmental Toolsuite," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 14-24.
  3. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
  4. Rehme, Michael F. & Franzelin, Fabian & Pflüger, Dirk, 2021. "B-splines on sparse grids for surrogates in uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
  5. Francesco Papi & Lorenzo Cappugi & Simone Salvadori & Mauro Carnevale & Alessandro Bianchini, 2020. "Uncertainty Quantification of the Effects of Blade Damage on the Actual Energy Production of Modern Wind Turbines," Energies, MDPI, vol. 13(15), pages 1-18, July.
  6. 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).
  7. Cheng, Hongzhi & Zhou, Chuangxin & Lu, Xingen & Zhao, Shengfeng & Han, Ge & Yang, Chengwu, 2023. "Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties," Energy, Elsevier, vol. 278(PB).
  8. 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).
  9. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
  10. Papi, Francesco & Balduzzi, Francesco & Ferrara, Giovanni & Bianchini, Alessandro, 2021. "Uncertainty quantification on the effects of rain-induced erosion on annual energy production and performance of a Multi-MW wind turbine," Renewable Energy, Elsevier, vol. 165(P1), pages 701-715.
  11. Abgrall, R. & Congedo, P.M. & Geraci, G., 2017. "Towards a unified multiresolution scheme for treating discontinuities in differential equations with uncertainties," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 139(C), pages 1-22.
  12. Xiao, Sinan & Praditia, Timothy & Oladyshkin, Sergey & Nowak, Wolfgang, 2021. "Global sensitivity analysis of a CaO/Ca(OH)2 thermochemical energy storage model for parametric effect analysis," Applied Energy, Elsevier, vol. 285(C).
  13. Lim, HyeongUk & Manuel, Lance, 2021. "Distribution-free polynomial chaos expansion surrogate models for efficient structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
  14. Marius Marinescu & Alberto Olivares & Ernesto Staffetti & Junzi Sun, 2023. "Polynomial Chaos Expansion-Based Enhanced Gaussian Process Regression for Wind Velocity Field Estimation from Aircraft-Derived Data," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
  15. Qin, Zhiyuan & Naser, M.Z., 2023. "Machine learning and model driven bayesian uncertainty quantification in suspended nonstructural systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  16. Iftikhar Ahmad & Ahsan Ayub & Uzair Ibrahim & Mansoor Khan Khattak & Manabu Kano, 2018. "Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process," Energies, MDPI, vol. 12(1), pages 1-13, December.
  17. Xie, Qimiao & Wang, Jinhui & Lu, Shouxiang & Hensen, Jan L.M., 2016. "An optimization method for the distance between exits of buildings considering uncertainties based on arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 188-196.
  18. Lin, Y.-T. & Shih, Y.-T. & Chien, C.-S. & Sheng, Q., 2021. "A note on stochastic polynomial chaos expansions for uncertain volatility and Asian option pricing," Applied Mathematics and Computation, Elsevier, vol. 393(C).
  19. Olivares, Alberto & Staffetti, Ernesto, 2023. "A statistical moment-based spectral approach to the chance-constrained stochastic optimal control of epidemic models," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
  20. 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).
  21. Luca Di Persio & Michele Bonollo & Gregorio Pellegrini, 2015. "A computational spectral approach to interest rate models," Papers 1508.06236, arXiv.org.
  22. Xie, Xiangzhong & Schenkendorf, René & Krewer, Ulrike, 2019. "Efficient sensitivity analysis and interpretation of parameter correlations in chemical engineering," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 159-173.
  23. 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.
  24. Shengwen Yin & Keliang Jin & Yu Bai & Wei Zhou & Zhonggang Wang, 2023. "Solution-Space-Reduction-Based Evidence Theory Method for Stiffness Evaluation of Air Springs with Epistemic Uncertainty," Mathematics, MDPI, vol. 11(5), pages 1-19, March.
  25. Xiao, Sinan & Oladyshkin, Sergey & Nowak, Wolfgang, 2020. "Reliability analysis with stratified importance sampling based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
  26. Zhai, Qingqing & Yang, Jun & Zhao, Yu, 2014. "Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 66-82.
  27. Olivares, Alberto & Staffetti, Ernesto, 2021. "Uncertainty quantification of a mathematical model of COVID-19 transmission dynamics with mass vaccination strategy," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
  28. de Cursi, Eduardo Souza, 2021. "Uncertainty quantification in game theory," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
  29. Zheng, Xiaohu & Yao, Wen & Zhang, Yunyang & Zhang, Xiaoya, 2022. "Consistency regularization-based deep polynomial chaos neural network method for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
  30. Jiang, Chen & Vega, Manuel A. & Todd, Michael D. & Hu, Zhen, 2022. "Model correction and updating of a stochastic degradation model for failure prognostics of miter gates," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
  31. Guan, Xuefei, 2024. "Sparse moment quadrature for uncertainty modeling and quantification," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  32. 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).
  33. J. Yang & B. Faverjon & D. Dureisseix & P. Swider & S. Marburg & H. Peters & N. Kessissoglou, 2016. "Prediction of the intramembranous tissue formation during perisprosthetic healing with uncertainties. Part 2. Global clinical healing due to combination of random sources," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 19(13), pages 1387-1394, October.
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