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77


Journal articles38 documents

  • A. Marrel, Bertrand Iooss, V Chabridon. The ICSCREAM methodology: Identification of penalizing configurations in computer experiments using screening and metamodel -- Applications in thermal-hydraulics. Nuclear Science and Engineering, Academic Press, 2022, 196, pp.301-321. ⟨10.1080/00295639.2021.1980362⟩. ⟨hal-02535146v4⟩
  • Clement Gauchy, Jerome Stenger, Roman Sueur, Bertrand Iooss. An information geometry approach for robustness analysis in uncertainty quantification of computer codes. Technometrics, Taylor & Francis, 2022, 64, pp.80-91. ⟨hal-02425477v3⟩
  • Charles Demay, Bertrand Iooss, Loïc Le Gratiet, Amandine Marrel. Model selection for Gaussian Process regression: an application with highlights on the model variance validation. Quality and Reliability Engineering International, Wiley, 2021. ⟨hal-03207216⟩
  • Sibo Cheng, Jean-Philippe Argaud, Bertrand Iooss, Didier Lucor, Angélique Ponçot. Error covariance tuning in variational data assimilation: application to an operating hydrological model. Stochastic Environmental Research and Risk Assessment, Springer Verlag (Germany), 2021, ⟨10.1007/s00477-020-01933-7⟩. ⟨hal-02992507⟩
  • Marouane Il Idrissi, Vincent Chabridon, Bertrand Iooss. Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs. Environmental Modelling & Software, Elsevier, 2021. ⟨hal-03106452v3⟩
  • Alvaro Rollon de Pinedo, Mathieu Couplet, Bertrand Iooss, Nathalie Marie, Amandine Marrel, et al.. Functional outlier detection by means of h-mode depth and dynamic time warping. Applied Sciences, MDPI, 2021, 11 (23), pp.11475. ⟨10.3390/app112311475⟩. ⟨hal-02965504v2⟩
  • Etienne Le Mire, Emilien Burger, Bertrand Iooss, Chu Mai. Prediction of crack propagation kinetics through multipoint stochastic simulations of microscopic fields. EPJ N - Nuclear Sciences & Technologies, EDP Sciences, 2021, 7 (4). ⟨hal-02068315v2⟩
  • Bertrand Iooss, V Vergès, V Larget. BEPU robustness analysis via perturbed-law based sensitivity indices. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, SAGE Publications, In press, ⟨10.1177/1748006X211036569⟩. ⟨hal-02864053v4⟩
  • Sibo Cheng, Jean-Philippe Argaud, Bertrand Iooss, Angélique Ponçot, Didier Lucor. A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping. Mathematical Geosciences, Springer Verlag, 2021. ⟨meteo-02460851v2⟩
  • Jean Baccou, Jinzhao Zhang, Philippe Fillion, Guillaume Damblin, Alessandro Petruzzi, et al.. SAPIUM: a generic framework for a practical and transparent quantification of thermal hydraulic code model input uncertainty. Nuclear Science and Engineering, Academic Press, 2020, 194, pp.721-736. ⟨10.1080/00295639.2020.1759310⟩. ⟨hal-03149294⟩
  • Jerome Stenger, Fabrice Gamboa, Merlin M. Keller, Bertrand Iooss. Optimal Uncertainty Quantification of a risk measurement from a thermal-hydraulic code using Canonical Moments. International Journal of Uncertainty Quantification, Begell House, 2020, 10, pp.35-53. ⟨hal-01987449v2⟩
  • Alejandro Ribes, Joachim Pouderoux, Bertrand Iooss. A Visual Sensitivity Analysis for Parameter- Augmented Ensembles of Curves. The Journal of Verification, Validation and Uncertainty Quantification (VVUQ), The American Society of Mechanical Engineers (ASME), 2019, 4 (4), ⟨10.1115/1.4046020⟩. ⟨hal-02490440⟩
  • Bertrand Iooss, Amandine Marrel. Advanced methodology for uncertainty propagation in computer experiments with large number of inputs. Nuclear Technology, American Nuclear Society, 2019, 205, pp.1588-1606. ⟨hal-01907198v2⟩
  • Sibo Cheng, Jean-Philippe Argaud, Bertrand Iooss, Didier Lucor, Angélique Ponçot. Background Error Covariance Iterative Updating with Invariant Observation Measures for Data Assimilation. Stochastic Environmental Research and Risk Assessment, Springer Verlag (Germany), 2019, 33, pp.2033-2051. ⟨hal-02307657⟩
  • Bertrand Iooss, Loïc Le Gratiet. Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes. Reliability Engineering and System Safety, Elsevier, 2019, 187, pp.58-66. ⟨hal-01357005v3⟩
  • Bertrand Iooss, Clémentine Prieur. Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications. International Journal for Uncertainty Quantification, Begell House Publishers, 2019, 9 (5), pp.493-514. ⟨10.1615/Int.J.UncertaintyQuantification.2019028372⟩. ⟨hal-01556303v7⟩
  • Loïc Le Gratiet, Bertrand Iooss, Géraud Blatman, Thomas Browne, Sara Cordeiro, et al.. Model Assisted Probability of Detection curves: New statistical tools and progressive methodology. Journal of Nondestructive Evaluation, Springer Verlag, 2017, 36 (1). ⟨hal-01260335⟩
  • Olivier Roustant, Franck Barthe, Bertrand Iooss. Poincaré inequalities on intervals – application to sensitivity analysis. Electronic Journal of Statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2017, 11 (2), pp.3081 - 3119. ⟨10.1214/17-EJS1310⟩. ⟨hal-01388758v2⟩
  • Géraud Blatman, Thibault Delage, Bertrand Iooss, Nadia Pérot. Probabilistic risk bounds for the characterization of radiological contamination. EPJ N - Nuclear Sciences & Technologies, EDP Sciences, 2017, 3. ⟨hal-01413664v2⟩
  • Alberto Pasanisi, Pierre Barbillon, Bertrand Iooss, Hervé Monod. Éditorial du numéro spécial : Expériences numériques, analyse d'incertitude et de sensibilité. Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2017, 158 (1), pp.1. ⟨hal-01606787⟩
  • Tatiana Labopin-Richard, Fabrice Gamboa, Aurélien Garivier, Bertrand Iooss. Bregman superquantiles. Estimation methods and applications.. Dependence Modeling, De Gruyter, 2016, 4 (1), pp.1-33. ⟨hal-00996440v8⟩
  • Thomas Browne, Bertrand Iooss, Loïc Le Gratiet, Jérôme Lonchampt, Emmanuel Remy. Stochastic simulators based optimization by Gaussian process metamodels -Application to maintenance investments planning issues Short title: Metamodel-based optimization of stochastic simulators. Quality and Reliability Engineering International, Wiley, 2016, 32 (6). ⟨hal-01242478v2⟩
  • Loic Le Gratiet, Claire Cannamela, Bertrand Iooss. A Bayesian approach for global sensitivity analysis of (multi-fidelity) computer codes. SIAM/ASA Journal on Uncertainty Quantification, ASA, American Statistical Association, 2014, 2, pp.336-363. ⟨hal-00842432⟩
  • Matieyendou Lamboni, Bertrand Iooss, Anne-Laure Popelin, Fabrice Gamboa. Derivative-based global sensitivity measures: general links with Sobol' indices and numerical tests. Mathematics and Computers in Simulation, Elsevier, 2013, 87, pp.45-54. ⟨hal-00666473v2⟩
  • Guillaume Damblin, Mathieu Couplet, Bertrand Iooss. Numerical studies of space filling designs: optimization of Latin Hypercube Samples and subprojection properties. Journal of Simulation, Palgrave Macmillan, 2013, 7, pp.276-289. ⟨hal-00848240⟩
  • Benjamin Auder, Agnes de Crecy, Bertrand Iooss, Michel Marques. Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations. Reliability Engineering and System Safety, Elsevier, 2012, 107, pp.122-131. ⟨hal-00525491v2⟩
  • Amandine Marrel, Bertrand Iooss, Sébastien da Veiga, Mathieu Ribatet. Global sensitivity analysis of stochastic computer models with joint metamodels. Statistics and Computing, Springer Verlag (Germany), 2012, 22, pp.833-847. ⟨10.1007/s11222-011-9274-8⟩. ⟨hal-00525489v2⟩
  • Amandine Marrel, Bertrand Iooss, Michel Jullien, Béatrice Laurent, Elena Volkova. Global sensitivity analysis for models with spatially dependent outputs. Environmetrics, Wiley, 2011, 22, pp.383-397. ⟨hal-00430171v4⟩
  • Bertrand Iooss. Revue sur l'analyse de sensibilité globale de modèles numériques. Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2011, 152 (1), pp.1-23. ⟨hal-00503179v2⟩
  • Olivier Asserin, Alexandre Loredo, Matthieu Petelet, Bertrand Iooss. Global sensitivity analysis in welding simulations -- what are the material data you really need ?. Finite Elements in Analysis and Design, Elsevier, 2011, 47 (9), pp.1004-1016. ⟨10.1016/j.finel.2011.03.016⟩. ⟨hal-00419162⟩
  • Bertrand Iooss, Loïc Boussouf, Vincent Feuillard, Amandine Marrel. Numerical studies of the metamodel fitting and validation processes. International Journal On Advances in Systems and Measurements, IARIA, 2010, 3, pp.11-21. ⟨hal-00444666v2⟩
  • Matthieu Petelet, Bertrand Iooss, Olivier Asserin, Alexandre Loredo. Latin hypercube sampling with inequality constraints. AStA Advances in Statistical Analysis, Springer Verlag, 2010, 94, pp.325-339. ⟨hal-00412235v3⟩
  • Amandine Marrel, Bertrand Iooss, Béatrice Laurent, Olivier Roustant. Calculations of Sobol indices for the Gaussian process metamodel. Reliability Engineering and System Safety, Elsevier, 2009, 94, pp.742-751. ⟨10.1016/j.ress.2008.07.008⟩. ⟨hal-00239494⟩
  • Amandine Marrel, Bertrand Iooss, Sébastien da Veiga, Mathieu Ribatet. Global sensitivity analysis of stochastic computer models with generalized additive models. Statistics and Computing, Springer Verlag (Germany), 2009, 22 (3), pp.833-847. ⟨10.1007/s11222-011-9274-8⟩. ⟨cea-02355755⟩
  • Bertrand Iooss, Mathieu Ribatet. Global sensitivity analysis of computer models with functional inputs. Reliability Engineering and System Safety, Elsevier, 2009, 94 (7), pp.1194-1204. ⟨hal-00243156v2⟩
  • Amandine Marrel, Bertrand Iooss, Francois van Dorpe, Elena Volkova. An efficient methodology for modeling complex computer codes with Gaussian processes. Computational Statistics and Data Analysis, Elsevier, 2008, 52, pp.4731-4744. ⟨10.1016/j.csda.2008.03.026⟩. ⟨hal-00239492v2⟩
  • Claire Cannamela, Josselin Garnier, Bertrand Iooss. Controlled stratification for quantile estimation. Annals of Applied Statistics, Institute of Mathematical Statistics, 2008, 2 (4), pp.1554 - 1580. ⟨10.1214/08-AOAS186⟩. ⟨cea-02514913⟩
  • Claire Cannamela, Josselin Garnier, Bertrand Iooss. Controlled stratification for quantile estimation. Annals of Applied Statistics, Institute of Mathematical Statistics, 2008, 2 (4), pp.1554-1580. ⟨hal-00256644⟩

Conference papers18 documents

  • Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss, Jean-Michel Loubes. Projection de mesures de probabilité sous contraintes de quantile par distance de Wasserstein et approximation monotone polynomiale. 53èmes Journées de Statistique de la Société Française de Statistique (SFdS), Jun 2022, Lyon, France. ⟨hal-03597059⟩
  • Amandine Marrel, Bertrand Iooss, Vincent Chabridon. The ICSCREAM methodology: identification of penalizing configurations in computer experiments using screening and metamodel applications in thermal-hydraulics. UQ22 - SIAM Conference on Uncertainty Quantification, SIAM, Apr 2022, Atlanta, United States. ⟨cea-03700747⟩
  • Bertrand Iooss. Sample selection from a given dataset to validate machine learning models. 50th Meeting of the Italian Statistical Society (SIS2021), Jun 2021, Pisa, Italy. ⟨hal-03208245⟩
  • Bertrand Iooss, Jérôme Lonchampt. Robust tuning of Robbins-Monro algorithm for quantile estimation - Application to wind-farm asset management. ESREL 2021, Sep 2021, Angers, France. ⟨hal-03191621⟩
  • Marouane Il Idrissi, Bertrand Iooss, Vincent Chabridon. Mesures d'importance relative par décomposition de la performance de modèles de régression. 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), Jun 2021, Nice, France. ⟨hal-03149764⟩
  • Bertrand Iooss. Estimation itérative en propagation d'incertitudes : réglage robuste de l'algorithme de Robbins-Monro. 52èmes Journées de Statistiques de la Société Française de Statistique (SFdS), 2020, Nice, France. pp.466-471. ⟨hal-02511787⟩
  • Jean-Philippe Argaud, Sibo Cheng, Bertrand Iooss, Didier Lucor, Angélique Ponçot. Iterative methods for improving error covariance modeling in variational assimilation. International Conference on Uncertainty Quantification in Computational Sciences and Engineering, M. Papadrakakis, V. Papadopoulos, G. Stefanou, Jun 2019, Crete, Greece. ⟨hal-02397315⟩
  • A. Marrel, Bertrand Iooss. Advanced methodology for uncertainty propagation in computer experiments with large number of inputs application to accidental scenario in a Pressurized water Reactor. European Safety and Reliability Conference (ESREL - 2018), Jun 2018, Trondheim, Norway. ⟨hal-02415316⟩
  • B. Iooss, A. Marrel. Advanced methodology for uncertainty propagation in computer experiments with large number of inputs. ANS Best Estimate Plus Uncertainty International Conference (BEPU 2018), May 2018, Lucca, Italy. ⟨cea-02339307⟩
  • Bertrand Iooss, Amandine Marrel. An efficient methodology for the analysis and modeling of computer experiments with large number of inputs. UNCECOMP 2017 2nd ECCOMAS Thematic Conference onUncertainty Quantification in Computational Sciences and Engineering, Jun 2017, Rhodes Island, Greece. pp.187-197, ⟨10.7712/120217.5362.16891⟩. ⟨hal-01511505⟩
  • Roman Sueur, Bertrand Iooss, Thibault Delage. Sensitivity analysis using perturbed-law based indices for quantiles and application to an industrial case. 10th International Conference on Mathematical Methods in Reliability (MMR 2017), Jul 2017, Grenoble, France. ⟨hal-01552361⟩
  • Théophile Terraz, Alejandro Ribes, Yvan Fournier, Bertrand Iooss, Bruno Raffin. Melissa: Large Scale In Transit Sensitivity Analysis Avoiding Intermediate Files. The International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing), Nov 2017, Denver, United States. pp.1 - 14. ⟨hal-01607479⟩
  • Roman Sueur, Nicolas Bousquet, Bertrand Iooss, Julien Bect. Perturbed-Law based sensitivity Indices for sensitivity analysis in structural reliability. 8th International Conference on Sensitivity Analysis of Model Output (SAMO 2016), Nov 2016, Le Tampon, Réunion, France. pp.89-90. ⟨hal-01569578⟩
  • Thomas Browne, Bertrand Iooss, Loïc Le Gratiet, Jérome Lonchampt. Stochastic simulators based optimization by Gaussian process metamodels - Application to maintenance investments planning issues. ENBIS 2015, Sep 2015, Prague, Czech Republic. ⟨hal-01198463⟩
  • Julien Bect, Nicolas Bousquet, Bertrand Iooss, Shijie Liu, Alice Mabille, et al.. Quantification et réduction de l'incertitude concernant les propriétés de monotonie d'un code de calcul coûteux à évaluer. 46èmes Journées de Statistique de la SFdS (JdS 2014), Jun 2014, Rennes, France. 6 p. ⟨hal-01057322⟩
  • Anne-Laure Popelin, Bertrand Iooss. Visualization tools for uncertainty and sensitivity analyses on thermal-hydraulic transients. Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo 2013 (SNA + MC 2013), Oct 2013, Paris, France. pp.1. ⟨hal-00952930⟩
  • Benjamin Auder, Bertrand Iooss. Clustering "optimal" dans des espaces fonctionnels. 41èmes Journées de Statistique, SFdS, Bordeaux, 2009, Bordeaux, France, France. ⟨inria-00386593⟩
  • Bertrand Iooss, M. Ribatet. Global sensitivity analysis of stochastic computer models. 38ièmes Journées de Statistiques, Clamart, 29 mai-2 juin 2006, 2006, pp.17. ⟨hal-02588584⟩

Conference poster1 document

  • Julien Bect, Nicolas Bousquet, Bertrand Iooss, Shijie Liu, Alice Mabille, et al.. Uncertainty quantification and reduction for the monotonicity properties of expensive-to-evaluate computer models. Uncertainty in Computer Models 2014 Conference, Jul 2014, Sheffield, United Kingdom. ⟨hal-01103724⟩

Books2 documents

  • Sébastien da Veiga, Fabrice Gamboa, Bertrand Iooss, Clémentine Prieur. Basics and Trends in Sensitivity Analysis: Theory and Practice in R. SIAM publisher. Computational science and engineering. SIAM, pp.1-16, 2021. ⟨hal-03526153⟩
  • Robert Faivre, Bertrand Iooss, Stéphanie Mahévas, David Makowski, Herve Monod. Analyse de sensibilité et exploration de modèles : Application aux sciences de la nature et de l'environnement. Editions Quae, 352 p., 2013, Collection Savoir-Faire, 978-2-7592-1906-3. ⟨hal-01173750⟩

Book sections6 documents

  • Elias Fekhari, Bertrand Iooss, Joseph Muré, Luc Pronzato, Maria-João Rendas. Model predictivity assessment: incremental test-set selection and accuracy evaluation. Studies in Theoretical and Applied Statistics, Springer, In press. ⟨hal-03523695v3⟩
  • Serge Kucherenko, Bertrand Iooss. Derivative based global sensitivity measures. R. Ghanem, D. Higdon and H. Owhadi (eds). Handbook of uncertainty quantification, Springer, 2017. ⟨hal-01079358v3⟩
  • Michaël Baudin, Anne Dutfoy, Bertrand Iooss, Anne-Laure Popelin. Title: Open TURNS: An industrial software for uncertainty quantification in simulation. R. Ghanem, D. Higdon and H. Owhadi. Handbook of uncertainty quantification, Springer, 2017. ⟨hal-01107849v2⟩
  • Bertrand Iooss, Paul Lemaître. A review on global sensitivity analysis methods. C. Meloni and G. Dellino. Uncertainty management in Simulation-Optimization of Complex Systems: Algorithms and Applications, Springer, 2015. ⟨hal-00975701⟩
  • Bertrand Iooss, Hervé Monod, Thierry Faure, Lauriane Rouan. Echantillonnage en grande dimension. Analyse de sensibilité et exploration de modèles : : Application aux sciences de la nature et de l'environnement, Editions Quae, pp.436, 2013, Savoir Faire (Quae), 978-2-7592-1906-3. ⟨hal-02809397⟩
  • H. Richard, Hervé Monod, J. Wang, Jean Couteau, N. Dumoulin, et al.. La boîte à outils Mexico, un environnement générique pour piloter l'exploration numérique de modèles ( Chapitre 9). Analyse de sensibilité et exploration de modèles : application aux sciences de la nature et de l'environnement, Quae, pp.233-253, 2013, Savoir faire, 978-2-7592-1906-3. ⟨hal-02598481⟩

Other publications1 document

  • Bertrand Iooss, Mathieu Ribatet, Amandine Marrel. Global sensitivity analysis of stochastic computer models with joint metamodels. 2009. ⟨hal-00232805v3⟩

Preprints, Working Papers, ...8 documents

  • Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss, Jean-Michel Loubes. Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models. 2022. ⟨hal-03784768⟩
  • Nora Lüthen, Olivier Roustant, Fabrice Gamboa, Bertrand Iooss, Stefano Marelli, et al.. Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions. 2022. ⟨hal-03293672v2⟩
  • Martin Wieskotten, Marielle Crozet, Bertrand Iooss, Céline Lacaux, Amandine Marrel. A comparison between Bayesian and ordinary kriging based on validation criteria: application to radiological characterisation. 2022. ⟨hal-03806713⟩
  • Margot Herin, Marouane Il Idrissi, Vincent Chabridon, Bertrand Iooss. Proportional marginal effects for global sensitivity analysis. {date}. ⟨hal-03825935⟩
  • Bertrand Iooss. Robust tuning of Robbins-Monro algorithm for quantile estimation in iterative uncertainty quantification. 2020. ⟨hal-02918478⟩
  • Olivier Roustant, Fabrice Gamboa, Bertrand Iooss. Sensitivity Analysis and Generalized Chaos Expansions. Lower Bounds for Sobol indices. 2019. ⟨hal-02140127⟩
  • Thomas Browne, Jean-Claude Fort, Bertrand Iooss, Loïc Le Gratiet. Estimate of quantile-oriented sensitivity indices. 2017. ⟨hal-01450891⟩
  • Olivier Roustant, Jana Fruth, Bertrand Iooss, Sonja Kuhnt. Crossed-Derivative Based Sensitivity Measures for Interaction Screening. 2014. ⟨hal-00845446v2⟩

Reports2 documents

  • Alejandro Ribes, Théophile Terraz, Bertrand Iooss, Yvan Fournier, Bruno Raffin. Large scale in transit computation of quantiles for ensemble runs. [Research Report] EDF R&D. 2019. ⟨hal-02016828v2⟩
  • Paul Lemaître, Ekaterina Sergienko, Aurélie Arnaud, Nicolas Bousquet, Fabrice Gamboa, et al.. Density modification based reliability sensitivity analysis. 2012. ⟨hal-00737978v3⟩

Habilitation à diriger des recherches1 document

  • Bertrand Iooss. Contributions au traitement des incertitudes en modélisation numérique : propagation d'ondes en milieu aléatoire et analyse statistique d'expériences simulées. Mathématiques [math]. Université Paul Sabatier - Toulouse III, 2009. ⟨tel-00360995⟩