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Efficient computation of global sensitivity indices using sparse polynomial chaos expansions

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  1. Tsilifis, Panagiotis & Ghanem, Roger & Jacobs, Ryan, 2026. "Microstructure characterization in thermal barrier coatings using Sparse Polynomial Chaos Classifiers and Global Sensitivity Analysis," Reliability Engineering and System Safety, Elsevier, vol. 265(PA).
  2. Yue, Wenhao & Yang, Chen & Shi, Chenyue & Yang, Jinguang & Liao, Naibing, 2024. "Uncertainty quantification of the inlet boundary conditions in a supercritical CO2 centrifugal compressor based on the non-intrusive polynomial chaos," Energy, Elsevier, vol. 310(C).
  3. Steiner, M. & Bourinet, J.-M. & Lahmer, T., 2019. "An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 323-340.
  4. Chen, Xin & Molina-Cristóbal, Arturo & Guenov, Marin D. & Riaz, Atif, 2019. "Efficient method for variance-based sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 97-115.
  5. Wong, Chun Yui & Seshadri, Pranay & Parks, Geoffrey, 2021. "Extremum sensitivity analysis with polynomial Monte Carlo filtering," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
  6. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
  7. Lambert, Romain S.C. & Lemke, Frank & Kucherenko, Sergei S. & Song, Shufang & Shah, Nilay, 2016. "Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 128(C), pages 42-54.
  8. Wang, Zhenqiang & Jia, Gaofeng, 2020. "Augmented sample-based approach for efficient evaluation of risk sensitivity with respect to epistemic uncertainty in distribution parameters," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
  9. Shang, Xiaobing & Fang, Hai & Lu, Lingyun & Zhang, Zhi & Ma, Ping, 2026. "Partial gradient-enhanced multi-output sparse polynomial chaos expansion method for model representation," Reliability Engineering and System Safety, Elsevier, vol. 270(C).
  10. Hao, Donghui & Zhang, Jian & Yue, Xinxin & Chen, Lei, 2025. "Combined dimensionality reduction based adaptive polynomial chaos expansion for high-dimensional reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 264(PA).
  11. Deman, G. & Konakli, K. & Sudret, B. & Kerrou, J. & Perrochet, P. & Benabderrahmane, H., 2016. "Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 156-169.
  12. Keshtegar, Behrooz & Kisi, Ozgur, 2018. "RM5Tree: Radial basis M5 model tree for accurate structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 49-61.
  13. Awad, Mahmoud, 2017. "Analyzing sensitivity measures using moment-matching technique," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 90-99.
  14. Becker, William, 2020. "Metafunctions for benchmarking in sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
  15. Oladyshkin, S. & Nowak, W., 2012. "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 179-190.
  16. 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.
  17. Wu, Zeping & Wang, Wenjie & Wang, Donghui & Zhao, Kun & Zhang, Weihua, 2019. "Global sensitivity analysis using orthogonal augmented radial basis function," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 291-302.
  18. Wu, Zeping & Wang, Donghui & Okolo N, Patrick & Hu, Fan & Zhang, Weihua, 2016. "Global sensitivity analysis using a Gaussian Radial Basis Function metamodel," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 171-179.
  19. Haro Sandoval, Eduardo & Anstett-Collin, Floriane & Basset, Michel, 2012. "Sensitivity study of dynamic systems using polynomial chaos," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 15-26.
  20. Alexanderian, Alen & Gremaud, Pierre A. & Smith, Ralph C., 2020. "Variance-based sensitivity analysis for time-dependent processes," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
  21. Brown, S. & Beck, J. & Mahgerefteh, H. & Fraga, E.S., 2013. "Global sensitivity analysis of the impact of impurities on CO2 pipeline failure," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 43-54.
  22. Su, Maijia & Wang, Ziqi & Bursi, Oreste Salvatore & Broccardo, Marco, 2025. "Surrogate modeling for probability distribution estimation: Uniform or adaptive design?," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
  23. Cheng, Kai & Lu, Zhenzhou, 2018. "Sparse polynomial chaos expansion based on D-MORPH regression," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 17-30.
  24. Deman, G. & Kerrou, J. & Benabderrahmane, H. & Perrochet, P., 2015. "Sensitivity analysis of groundwater lifetime expectancy to hydro-dispersive parameters: The case of ANDRA Meuse/Haute-Marne site," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 276-286.
  25. Zhou, Yicheng & Lu, Zhenzhou & Yun, Wanying, 2020. "Active sparse polynomial chaos expansion for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
  26. Poëtte, Gaël, 2020. "Spectral convergence of the generalized Polynomial Chaos reduced model obtained from the uncertain linear Boltzmann equation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 24-45.
  27. Anstett-Collin, F. & Goffart, J. & Mara, T. & Denis-Vidal, L., 2015. "Sensitivity analysis of complex models: Coping with dynamic and static inputs," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 268-275.
  28. Guan, Xuefei, 2026. "The moment quadrature method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 265(PA).
  29. Edouard Dossetto & Christophe Chorro, 2022. "Building a global sensitivity analysis to quantify the robustness of macro-economic models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-05559166, HAL.
  30. Cremona, Marzia A. & Liu, Binbin & Hu, Yang & Bruni, Stefano & Lewis, Roger, 2016. "Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 49-59.
  31. Palar, Pramudita Satria & Zuhal, Lavi Rizki & Shimoyama, Koji & Tsuchiya, Takeshi, 2018. "Global sensitivity analysis via multi-fidelity polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 175-190.
  32. Zhang, Dequan & Liang, Hongyi & Li, Xing-ao & Jia, Xinyu & Wang, Fang, 2025. "Kinematic calibration of industrial robot using Bayesian modeling framework," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  33. Pronzato, Luc, 2019. "Sensitivity analysis via Karhunen–Loève expansion of a random field model: Estimation of Sobol’ indices and experimental design," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 93-109.
  34. Daneshkhah, Alireza & Bedford, Tim, 2013. "Probabilistic sensitivity analysis of system availability using Gaussian processes," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 82-93.
  35. Shang, Yue & Nogal, Maria & Teixeira, Rui & Wolfert, A.R. (Rogier) M., 2024. "Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
  36. 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).
  37. 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.
  38. Sibdari, Soheil & Mohammadian, Iman & Pyke, David F., 2018. "On the impact of jet fuel cost on airlines’ capacity choice: Evidence from the U.S. domestic markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 1-17.
  39. Xiang Peng & Xiaoqing Xu & Jiquan Li & Shaofei Jiang, 2021. "A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
  40. Li, Min & Wang, Ruo-Qian & Jia, Gaofeng, 2020. "Efficient dimension reduction and surrogate-based sensitivity analysis for expensive models with high-dimensional outputs," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  41. 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).
  42. Konakli, Katerina & Sudret, Bruno, 2016. "Global sensitivity analysis using low-rank tensor approximations," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 64-83.
  43. Touzani, Samir & Busby, Daniel, 2013. "Smoothing spline analysis of variance approach for global sensitivity analysis of computer codes," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 67-81.
  44. De Sanctis, Gianluca & Fontana, Mario, 2016. "Risk-based optimisation of fire safety egress provisions based on the LQI acceptance criterion," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 339-350.
  45. Zhang, Xufang & Pandey, Mahesh D., 2014. "An effective approximation for variance-based global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 164-174.
  46. Girard, Sylvain & Romary, Thomas & Favennec, Jean-Melaine & Stabat, Pascal & Wackernagel, Hans, 2013. "Sensitivity analysis and dimension reduction of a steam generator model for clogging diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 143-153.
  47. Verleysen, Kevin & Parente, Alessandro & Contino, Francesco, 2021. "How sensitive is a dynamic ammonia synthesis process? Global sensitivity analysis of a dynamic Haber-Bosch process (for flexible seasonal energy storage)," Energy, Elsevier, vol. 232(C).
  48. Garcia-Cabrejo, Oscar & Valocchi, Albert, 2014. "Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 25-36.
  49. Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
  50. Liu, Yaning & Yousuff Hussaini, M. & Ökten, Giray, 2016. "Accurate construction of high dimensional model representation with applications to uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 281-295.
  51. Sudret, B. & Mai, C.V., 2015. "Computing derivative-based global sensitivity measures using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 241-250.
  52. Miftakhova, Alena, 2021. "Global sensitivity analysis for optimal climate policies: Finding what truly matters," Economic Modelling, Elsevier, vol. 105(C).
  53. Lu, Ning & Li, Yan-Feng & Mi, Jinhua & Huang, Hong-Zhong, 2024. "AMFGP: An active learning reliability analysis method based on multi-fidelity Gaussian process surrogate model," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
  54. Borgonovo, Emanuele & Buzzard, Gregery T. & Wendell, Richard E., 2018. "A global tolerance approach to sensitivity analysis in linear programming," European Journal of Operational Research, Elsevier, vol. 267(1), pages 321-337.
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