1 code implementation • 15 Jan 2024 • Edgar Jaber, Vincent Blot, Nicolas Brunel, Vincent Chabridon, Emmanuel Remy, Bertrand Iooss, Didier Lucor, Mathilde Mougeot, Alessandro Leite
Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in industrial applications.
no code implementations • 10 Oct 2023 • Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss, Jean-Michel Loubes
The elements of this decomposition can be expressed using oblique projections and allow for novel interpretability indices for evaluation and variance decomposition purposes.
no code implementations • 18 Mar 2023 • Sibo Cheng, Cesar Quilodran-Casas, Said Ouala, Alban Farchi, Che Liu, Pierre Tandeo, Ronan Fablet, Didier Lucor, Bertrand Iooss, Julien Brajard, Dunhui Xiao, Tijana Janjic, Weiping Ding, Yike Guo, Alberto Carrassi, Marc Bocquet, Rossella Arcucci
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
1 code implementation • 23 Sep 2022 • Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss, Jean-Michel Loubes
Numerical experiments on real case studies, from the UQ and ML fields, highlight the computational feasibility of such studies and provide local and global insights on the robustness of black-box models to input perturbations.
no code implementations • 27 Apr 2021 • Bertrand Iooss
The selection of a validation basis from a full dataset is often required in industrial use of supervised machine learning algorithm.
1 code implementation • 20 Jan 2021 • Marouane Il Idrissi, Vincent Chabridon, Bertrand Iooss
This paper proposes new target sensitivity indices, based on the Shapley values and called "target Shapley effects", allowing for interpretable sensitivity measures under dependent inputs.
Statistics Theory Applications Methodology Statistics Theory
no code implementations • 7 Aug 2020 • Clement Gauchy, Jerome Stenger, Roman Sueur, Bertrand Iooss
Thus, a practical robustness analysis methodology should rely on a coherent definition of a distribution perturbation.
Statistics Theory Statistics Theory
no code implementations • 12 Dec 2016 • Olivier Roustant, Franck Barthe, Bertrand Iooss
We give semi-analytical results for some frequent distributions (truncated exponential, triangular, truncated normal), and present a numerical method in the general case.
2 code implementations • 21 Jan 2015 • Michaël Baudin, Anne Dutfoy, Bertrand Iooss, Anne-Laure Popelin
EDF R&D, Airbus Group and Phimeca Engineering started a collaboration at the beginning of 2005, joined by IMACS in 2014, for the development of an Open Source software platform dedicated to uncertainty propagation by probabilistic methods, named OpenTURNS for Open source Treatment of Uncertainty, Risk 'N Statistics.
Computation Statistics Theory Statistics Theory