Banière CIROQUO

Activités scientifiques

Le programme scientifque du consortium CIROQUO se décline autour des trois grands axes suivants
  • Axe 1 : Calibration, validation et transposition de codes
  • Axe 2 : Métamodélisation de codes dans des environnements complexes.
  • Axe 3 : Optimisation et inversion en présence d’incertitudes, en particulier pour des distributions de probabilités en sortie de code.
  • Axe 4 : Grande dimension.
De plus, une partie de l’activité du consortium portera sur le développement logiciel, sur la grande dimension et le lien avec le machine learning. Pour plus de détail cf Programme Scientifique:
Thèses
  • Charlie Sire, sept. 2020-, encadrement R. Le Riche (EMSE), Y. Richer (IRSN), J. Rohmer (BRGM). Titre : : Robust inversion under uncertainty for risk analysis – application to the failure of defences against flooding Résumé
  • Noé Fellmann, dec. 2021-, encadrement C. Helbert (ECL), C. Blanchet-Scalliet (ECL), A. Spagnol (IFPEN), D. Sinoquet (IFPEN). Titre : Sensitivity analysis for optimization problems under uncertainties Résumé
  • Adama Barry, jan. 2022-, encadrement F. Bachoc (IMT), C. Prieur (INRIA Grenoble), M. Munoz Zuniga (IFPEN). Titre : Plans d'expérience pour la calibration Résumé
  • Mohamed Gharafi, jan. 2022-, encadrement R. le Riche (EMSE), D. Brockhoff (INRIA Saclay), F. Huguet (Storengy). Titre : Stochastic and surrogate-assisted multi-objective optimization.
  • Tanguy Appriou, jan. 2022-, encadrement D. Rullière (EMSE), D. Gaudrie (Stellantis). Titre : Optimisation Bayesienne en grande dimension.
Post-Docs
  • Valentin BREAZ (INRIA Grenoble), jan. 2023-, Sujet
  • Chifaa DAHIK (IMT Tooulouse), oct. 2022-, Sujet
Publications
[1] A. F. López-Lopera, F. Bachoc, and O. Roustant. High-dimensional Additive Gaussian Processes under Monotonicity Constraints. In A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho, editors, Advances in Neural Information Processing Systems, 2022. [ http ]
[2] M. Gharafi, N. Hansen, D. Brockhoff, and R. Le Riche. Benchmarking of Two Implementations of CMA-ES with Diagonal Decoding on the bbob Test Suite. In GECCO 2022 Companion - The Genetic and Evolutionary Computation Conference, Boston, United States, July 2022. GECCO. [ DOI | http ]
[3] C. Sire, R. L. Riche, D. Rullière, J. Rohmer, L. Pheulpin, and Y. Richet. Quantizing rare random maps: application to flooding visualization. Journal of Computational and Graphical Statistics, 0(ja):1--31, 2023. [ DOI | arXiv | http ]
[4] M. Gharafi, N. Hansen, D. Brockhoff, and R. Le Riche. Multiobjective Optimization with a Quadratic Surrogate-Assisted CMA-ES. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '23, page 652–660, New York, NY, USA, 2023. Association for Computing Machinery. [ DOI | http ]
[5] T. Appriou, D. Rullière, and D. Gaudrie. Combination of Optimization-free Kriging Models for High-Dimensional Problems. . Computational Statistics, 2023. [ DOI | http ]
[6] M. Gharafi, N. Hansen, D. Brockhoff, and R. Le Riche. Multiobjective optimization with a quadratic surrogate-assisted CMA-ES. In GECCO 2023 - Genetic and Evolutionary Computation Conference, Lisbon, Portugal, July 2023. GECCO. [ DOI | http | .pdf ]
[7] J. Rohmer, C. Sire, S. Lecacheux, D. Idier, and R. Pedreros. Improved metamodels for predicting high-dimensional outputs by accounting for the dependence structure of the latent variables: application to marine flooding. Stochastic Environmental Research and Risk Assessment, 37(8):2919--2941, Aug. 2023. [ DOI | http ]
[8] C. Sire, Y. Richet, R. Le Riche, D. Rullière, J. Rohmer, and L. Pheulpin. FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations. 7 pages, 4 figures. Submitted to Journal Of Open Source Software, Aug. 2023. [ http ]
[9] C. Sire, D. Rullière, R. Le Riche, J. Rohmer, L. Pheulpin, and Y. Richet. Augmented quantization: a general approach to mixture models. 18 figures, 43 pages, Sept. 2023. [ http ]
[10] G. Perrin and R. Le Riche. Bayesian optimization with derivatives acceleration. working paper or preprint, Oct. 2023. [ http | .pdf ]
[11] N. Fellmann, M. Pasquier, C. Helbert, A. Spagnol, D. Sinoquet, and C. Blanchet-Scalliet. Sensitivity analysis for sets : application to pollutant concentration maps. working paper or preprint, Nov. 2023. [ http | .pdf ]
[12] O. Roustant, N. Lüthen, and F. Gamboa. Spectral decomposition of H1(μ) and Poincaré inequality on a compact interval — Application to kernel quadrature. Journal of Approximation Theory, 301:106041, 2024. [ DOI | http ]
[13] N. Fellmann, C. Blanchet-Scalliet, C. Helbert, A. Spagnol, and D. Sinoquet. Kernel-based Sensitivity Analysis for (excursion) sets. Technometrics, 0(ja):1--21, 2024. [ DOI | http ]
[14] V. Breaz, O. Zahm, and M. M. Munoz Zuniga. Literature review on rare event probability estimation in high dimension. working paper or preprint, 2024. [ http | .pdf ]
[15] T. Appriou, D. Rullière, and D. Gaudrie. High-dimensional Bayesian Optimization with a Combination of Kriging models. working paper or preprint, Feb. 2024. [ http | .pdf ]
[16] G. Lambert, C. Helbert, and C. Lauvernet. Quantization-based LHS for dependent inputs : application to sensitivity analysis of environmental models. working paper or preprint, Mar. 2024. [ http | .pdf ]
[17] A. Barry, F. Bachoc, S. Bouquet, M. M. Munoz Zuniga, and C. Prieur. Optimal Design of Physical and Numerical Experiments for Computer Code Calibration. working paper or preprint, June 2024. [ http | .pdf ]

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Publications connexes
[1] J. Rohmer, D. Idier, R. Thieblemont, G. Le Cozannet, and F. Bachoc. Partitioning the contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding. Natural Hazards and Earth System Sciences, 22(10):3167--3182, 2022. [ DOI | http ]
[2] H. Maatouk, X. Bay, and D. Rullière. A note on simulating hyperplane-truncated multivariate normal distributions. Statistics & Probability Letters, 191:109650, 2022. [ DOI | http ]
[3] C. Duhamel, C. Helbert, M. Munoz Zuniga, C. Prieur, and D. Sinoquet. A SUR version of the Bichon criterion for excursion set estimation. Statistics and Computing, 33(41):1573--1375, 2023. [ DOI | http ]
[4] M. R. El Amri, C. Helbert, M. Munoz Zuniga, C. Prieur, and D. Sinoquet. Feasible set estimation under functional uncertainty by Gaussian Process modelling. Physica D: Nonlinear Phenomena, 455:133893, 2023. [ DOI | http ]
[5] M. Menz, M. Munoz-Zuniga, and D. Sinoquet. Estimation of simulation failure set with active learning based on Gaussian Process classifiers and random set theory. working paper or preprint, Sept. 2023. [ http | .pdf ]
[6] H. Maatouk, D. Rullière, and X. Bay. Sampling large hyperplane-truncated multivariate normal distributions. Computational Statistics, Sept. 2023. available online, https://doi.org/10.1007/s00180-023-01416-7. [ DOI | http | .pdf ]
[7] Reyes, Adán Reyes, Nasr, André, Sinoquet, Delphine, and Hlioui, Sami. Study on the impact of uncertain design parameters on the performances of a permanent magnet-assisted synchronous reluctance motor. Sci. Tech. Energ. Transition, 79:13, 2024. [ DOI | http ]
[8] H. Maatouk, D. Rullière, and X. Bay. Bayesian Analysis of Constrained Gaussian Processes. Bayesian Analysis, pages 1 -- 30, 2024. [ DOI | http ]
[9] H. Maatouk, D. Rullière, and X. Bay. Large scale Gaussian processes with Matheron's update rule and Karhunen-Loève expansion. In A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022. Springer Verlag. 2024. [ http | .pdf ]

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