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Statistical Identification of Penalizing Configurations in High-dimensional Thermal-hydraulic Numerical Experiments: The ICSCREAM Methodology.

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Derivative-based global sensitivity measures: General links with Sobol' indices and numerical tests., , , and . Math. Comput. Simul., (2013)An Information Geometry Approach to Robustness Analysis for the Uncertainty Quantification of Computer Codes., , , and . Technometrics, 64 (1): 80-91 (2022)Melissa: large scale in transit sensitivity analysis avoiding intermediate files., , , , and . SC, page 61. ACM, (2017)Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review., , , , , , , , , and 7 other author(s). IEEE CAA J. Autom. Sinica, 10 (6): 1361-1387 (June 2023)Canonical Moments for Optimal Uncertainty Quantification on a Variety., , , and . GSI, volume 11712 of Lecture Notes in Computer Science, page 571-578. Springer, (2019)Visualizing statistical analysis of curve datasets in ParaView., , , and . IEEE VAST, page 267-268. IEEE Computer Society, (2014)Proportional Marginal Effects for Global Sensitivity Analysis., , , and . SIAM/ASA J. Uncertain. Quantification, 12 (2): 667-692 (2024)Contributions au traitement des incertitudes en modélisation numérique : propagation d'ondes en milieu aléatoire et analyse statistique d'expériences simulées. (Contributions to the uncertainty management in numerical modelisation: wave propagation in random media and analysis of computer experiments).. (2009)Calculations of Sobol indices for the Gaussian process metamodel., , , and . Reliab. Eng. Syst. Saf., 94 (3): 742-751 (2009)Statistical Identification of Penalizing Configurations in High-dimensional Thermal-hydraulic Numerical Experiments: The ICSCREAM Methodology., , and . CoRR, (2020)