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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 3T. Capelle, P. Sturm, A. Vidard, B. Morton.

    Calibration of the Tranus Land Use Module: Optimisation-Based Algorithms, their Validation, and Parameter Selection by Statistical Model Selection, in: Computers, Environment and Urban Systems, 2018. [ DOI : 10.1016/j.compenvurbsys.2017.04.009 ]

    https://hal.inria.fr/hal-01519654
  • 4C. Eldred, T. Dubos, E. Kritsikis.

    A Quasi-Hamiltonian Discretization of the Thermal Shallow Water Equations, in: Journal of Computational Physics, October 2018, pp. 1-53. [ DOI : 10.1016/j.jcp.2018.10.038 ]

    https://hal.inria.fr/hal-01847698
  • 5C. Eldred, D. Le Roux.

    Dispersion analysis of compatible Galerkin schemes for the 1D shallow water model, in: Journal of Computational Physics, October 2018, vol. 371, pp. 779-800. [ DOI : 10.1016/j.jcp.2018.06.007 ]

    https://hal.archives-ouvertes.fr/hal-01669048
  • 6N. Feyeux, A. Vidard, M. Nodet.

    Optimal transport for variational data assimilation, in: Nonlinear Processes in Geophysics, January 2018, vol. 25, no 1, pp. 55-66. [ DOI : 10.5194/npg-25-55-2018 ]

    https://hal.archives-ouvertes.fr/hal-01342193
  • 7L. Gilquin, E. Arnaud, C. Prieur, A. Janon.

    Making best use of permutations to compute sensitivity indices with replicated orthogonal arrays, in: Reliability Engineering and System Safety, October 2018, pp. 1-12. [ DOI : 10.1016/j.ress.2018.09.010 ]

    https://hal.inria.fr/hal-01558915
  • 8M. Gross, H. Wan, P. J. Rasch, P. M. Caldwell, D. L. Williamson, D. Klocke, C. Jablonowski, D. R. Thatcher, N. Wood, M. Cullen, B. Beare, M. Willett, F. Lemarié, E. Blayo, S. Malardel, P. Termonia, A. Gassmann, P. H. Lauritzen, H. Johansen, C. M. Zarzycki, K. Sakaguchi, R. Leung.

    Recent progress and review of Physics Dynamics Coupling in geophysical models, in: Monthly Weather Review, August 2018, https://arxiv.org/abs/1605.06480. [ DOI : 10.1175/MWR-D-17-0345.1 ]

    https://hal.inria.fr/hal-01323768
  • 9A. Janon, M. Nodet, C. Prieur, C. Prieur.

    Goal-oriented error estimation for parameter-dependent nonlinear problems, in: ESAIM: Mathematical Modelling and Numerical Analysis, July 2018, vol. 52, no 2, pp. 705-728. [ DOI : 10.1051/m2an/2018003 ]

    https://hal.archives-ouvertes.fr/hal-01290887
  • 10L. A. Jiménez Rugama, L. Gilquin.

    Reliable error estimation for Sobol' indices, in: Statistics and Computing, July 2018, vol. 28, no 4, pp. 725–738. [ DOI : 10.1007/s11222-017-9759-1 ]

    https://hal.inria.fr/hal-01358067
  • 11K. Klingbeil, F. Lemarié, L. Debreu, H. Burchard.

    The numerics of hydrostatic structured-grid coastal ocean models: state of the art and future perspectives, in: Ocean Modelling, May 2018, vol. 125, pp. 80-105. [ DOI : 10.1016/j.ocemod.2018.01.007 ]

    https://hal.inria.fr/hal-01443357
  • 12F. Lemarié, H. Burchard, L. Debreu, K. Klingbeil, J. Sainte-Marie.

    Advancing dynamical cores of oceanic models across all scales, in: Bulletin of the American Meteorological Society, November 2018. [ DOI : 10.1175/BAMS-D-18-0303.1 ]

    https://hal.inria.fr/hal-01939057
  • 13L. Li, A. Vidard, F.-X. Le Dimet, J. Ma.

    Topological data assimilation using Wasserstein distance, in: Inverse Problems, January 2019, vol. 35, no 1, 015006 p. [ DOI : 10.1088/1361-6420/aae993 ]

    https://hal.inria.fr/hal-01960206
  • 14V. Oerder, F. Colas, V. Echevin, S. Masson, F. Lemarié.

    Impacts of the Mesoscale Ocean-Atmosphere Coupling on the Peru-Chile Ocean Dynamics: The Current-Induced Wind Stress Modulation, in: Journal of Geophysical Research. Oceans, February 2018, vol. 123, no 2, pp. 812-833. [ DOI : 10.1002/2017JC013294 ]

    https://hal.inria.fr/hal-01661645
  • 15P. Tencaliec, A.-C. Favre, P. Naveau, C. Prieur.

    Flexible semiparametric Generalized Pareto modeling of the entire range of rainfall amount, in: Environmetrics, 2018, pp. 1-22.

    https://hal.inria.fr/hal-01709061

Invited Conferences

  • 16F. Auclair, R. Benshila, L. Debreu, N. Ducousso, F. Dumas, P. Marchesiello, F. Lemarié.

    Some Recent Developments around the CROCO Initiative for Complex Regional to Coastal Modeling, in: Comod Workshop on Coastal Ocean Modelling, Hambourg, Germany, February 2018.

    https://hal.inria.fr/hal-01947670
  • 17F. Lemarié, G. Samson, J.-L. Redelsperger, G. Madec, H. Giordani, R. Bourdalle-Badie, Y. Drillet.

    PPR SIMBAD: en quête d’une nouvelle méthodologie de représentation des échanges air-mer dans les modèles opérationnels globaux d’océan à haute-résolution, in: Colloque de Bilan et de Prospective du programme LEFE, Clermond-Ferrand, France, March 2018.

    https://hal.inria.fr/hal-01947683
  • 18O. Zahm, P. Constantine, C. Prieur, Y. Marzouk.

    Certified dimension reduction of the input parameter space of vector-valued functions, in: INI Workshop UNQW03, Cambridge, United Kingdom, March 2018.

    https://hal.inria.fr/hal-01955776
  • 19O. Zahm, P. Constantine, C. Prieur, Y. Marzouk.

    Certified dimension reduction of the input parameter space of vector-valued functions, in: FrontUQ 18 - Frontiers of Uncertainty Quantification, Pavie, Italy, September 2018.

    https://hal.inria.fr/hal-01955806
  • 20O. Zahm, Y. Marzouk, C. Prieur, P. Constantine.

    Certified dimension reduction of the input parameter space of Bayesian inverse problems, in: IMS Vilnius - 12th International Vilnius Conference on Probability Theory and Mathematical Statistics, Vilnius, Lithuania, July 2018.

    https://hal.inria.fr/hal-01955800
  • 21O. Zahm.

    Certified dimension reduction of the input parameter space of multivariate functions, in: Journées EDP Auvergne-Rhône-Alpes, Grenoble, France, November 2018.

    https://hal.inria.fr/hal-01955812
  • 22O. Zahm.

    Detecting and exploiting the low-effective dimension of multivariate problems using gradient information, in: Séminaire MATHICSE, EPFL, Lausanne, Switzerland, November 2018.

    https://hal.inria.fr/hal-01955818
  • 23O. Zahm.

    Dimension reduction of the input parameter space of vector-valued functions, in: SIAM-UQ 2018 - SIAM Conference on Uncertainty Quantification, Los Angeles, United States, April 2018.

    https://hal.inria.fr/hal-01955795

Conferences without Proceedings

  • 24F. Auclair, L. Debreu.

    A non-hydrostatic non Boussinesq algorithm for free surface ocean modelling, in: COMMODORE: Community for the numerical modeling of the global, regional and coastal ocean, Paris, France, September 2018.

    https://hal.inria.fr/hal-01961579
  • 25E. Blayo, F. Lemarié, C. Pelletier, S. Théry.

    Toward improved ocean-atmosphere coupling algorithms, in: 25th international conference on Domain Decomposition Methods, St. John's, Canada, July 2018.

    https://hal.inria.fr/hal-01951472
  • 26E. Blayo, A. Rousseau.

    Coupling hydrostatic and nonhydrostatic Navier-Stokes flows using a Schwarz algorithm, in: 25th international conference on Domain Decomposition Methods, St. John's, Canada, July 2018.

    https://hal.inria.fr/hal-01951485
  • 27F. Lemarié.

    On the discretization of vertical diffusion in the turbulent surface and planetary boundary layers, in: 3rd workshop on Physics Dynamics Coupling (PDC18), Reading, United Kingdom, July 2018.

    https://hal.inria.fr/hal-01947691
  • 28S. Théry, E. Blayo, F. Lemarié.

    Algorithmes de Schwarz et conditions absorbantes pour le couplage océan-atmosphère, in: Congrès National d'Analyse Numérique, Cap d'Agde, France, May 2018.

    https://hal.inria.fr/hal-01947885
  • 29O. Zahm.

    Dimension reduction of the input parameter space of vector-valued functions, in: MoRePaS 2018 - Model Reduction of Parametrized Systems IV, Nantes, France, April 2018.

    https://hal.inria.fr/hal-01955788

Scientific Popularization

  • 30E. Blayo.

    Les big data peuvent-ils faire la pluie et le beau temps ?, Le Monde, October 2018.

    https://hal.inria.fr/hal-01951505
  • 31S. Dewyspelaere, M. Nodet, J. Charton, P. Garat, F. Letue, C. Pès, V. Wales.

    Exemple d'EPI au collège : l'évolution des glaciers, in: Repères IREM, July 2018, no 112.

    https://hal.inria.fr/hal-01619788

Other Publications

References in notes
  • 47A. Beljaars, E. Dutra, G. Balsamo, F. Lemarié.

    On the numerical stability of surface-atmosphere coupling in weather and climate models, in: Geoscientific Model Development Discussions, 2017, vol. 10, no 2, pp. 977-989. [ DOI : 10.5194/gmd-10-977-2017 ]

    https://hal.inria.fr/hal-01406623
  • 48P. Cattiaux, J. R. Leon, C. Prieur.

    Estimation for Stochastic Damping Hamiltonian Systems under Partial Observation. I. Invariant density, in: Stochastic Processes and their Applications, March 2014, vol. 124, no 3, pp. 1236-1260. [ DOI : 10.1016/j.spa.2013.10.008 ]

    https://hal.archives-ouvertes.fr/hal-00739136
  • 49P. Cattiaux, J. R. Leon, C. Prieur.

    Estimation for Stochastic Damping Hamiltonian Systems under Partial Observation. II Drift term, in: ALEA (Latin American Journal of Probability and Statistics), 2014, vol. 11, no 1, pp. 359-384.

    https://hal.archives-ouvertes.fr/hal-00877054
  • 50P. Cattiaux, J. R. Leon, C. Prieur.

    Recursive Estimation for Stochastic Damping Hamiltonian Systems, in: Journal of Nonparametric Statistics, 2015, vol. 27, no 3, pp. 401-424.

    https://hal.archives-ouvertes.fr/hal-01071252
  • 51P. Cattiaux, J. R. León, A. Pineda Centeno, C. Prieur.

    An overlook on statistical inference issues for stochastic damping Hamiltonian systems under the fluctuation-dissipation condition, in: Statistics, 2017, vol. 51, no 1, pp. 11-29. [ DOI : 10.1080/02331888.2016.1259807 ]

    https://hal.archives-ouvertes.fr/hal-01405427
  • 52M. Champion, G. Chastaing, S. Gadat, C. Prieur.

    L2 Boosting on generalized Hoeffding decomposition for dependent variables. Application to Sensitivity Analysis, 2013, 48 pages, 7 Figures.
  • 53G. Chastaing.

    Generalized Sobol sensitivity indices for dependent variables, Université de Grenoble, September 2013.

    https://tel.archives-ouvertes.fr/tel-00930229
  • 54G. Chastaing, F. Gamboa, C. Prieur.

    Generalized Hoeffding-Sobol Decomposition for Dependent Variables - Application to Sensitivity Analysis, in: Electronic Journal of Statistics, December 2012, vol. 6, pp. 2420-2448. [ DOI : 10.1214/12-EJS749 ]

    http://hal.archives-ouvertes.fr/hal-00649404
  • 55G. Chastaing, C. Prieur, F. Gamboa.

    Generalized Sobol sensitivity indices for dependent variables: numerical methods, March 2013.

    http://hal.inria.fr/hal-00801628
  • 56A. Cousin, E. Di Bernardino.

    On multivariate extensions of Value-at-Risk, in: J. Multivariate Anal., 2013, vol. 119, pp. 32–46.

    http://dx.doi.org/10.1016/j.jmva.2013.03.016
  • 57C. De Michele, G. Salvadori, R. Vezzoli, S. Pecora.

    Multivariate assessment of droughts: Frequency analysis and dynamic return period, in: Water Resources Research, 2013, vol. 49, no 10, pp. 6985–6994.
  • 58E. Di Bernardino, T. Laloë, V. Maume-Deschamps, C. Prieur.

    Plug-in estimation of level sets in a non-compact setting with applications in multivariate risk theory, in: ESAIM: Probability and Statistics, February 2013, vol. 17, pp. 236-256. [ DOI : 10.1051/ps/2011161 ]

    https://hal.archives-ouvertes.fr/hal-00580624
  • 59E. Di Bernardino, V. Maume-Deschamps, C. Prieur.

    Estimating Bivariate Tail: a copula based approach, in: Journal of Multivariate Analysis, August 2013, vol. 119, pp. 81-100. [ DOI : 10.1016/j.jmva.2013.03.020 ]

    https://hal.archives-ouvertes.fr/hal-00475386
  • 60E. Di Bernardino, C. Prieur.

    Estimation of Multivariate Conditional Tail Expectation using Kendall's Process, in: Journal of Nonparametric Statistics, March 2014, vol. 26, no 2, pp. 241-267. [ DOI : 10.1080/10485252.2014.889137 ]

    https://hal.archives-ouvertes.fr/hal-00740340
  • 61L. Gilquin, E. Arnaud, C. Prieur, H. Monod.

    Recursive estimation procedure of Sobol' indices based on replicated designs, January 2016, working paper or preprint.

    https://hal.inria.fr/hal-01291769
  • 62L. Gilquin.

    Monte Carlo and quasi-Monte Carlo sampling methods for the estimation of Sobol' indices. Application to a LUTI model, Université Grenoble Alpes, October 2016.

    https://hal.inria.fr/tel-01403914
  • 63L. Gilquin, C. Prieur, E. Arnaud.

    Replication procedure for grouped Sobol' indices estimation in dependent uncertainty spaces, in: Information and Inference, August 2015, vol. 4, no 4, pp. 354-379. [ DOI : 10.1093/imaiai/iav010 ]

    https://hal.inria.fr/hal-01045034
  • 64M. Gross, H. Wan, P. J. Rasch, P. M. Caldwell, D. L. Williamson, D. Klocke, C. Jablonowski, D. R. Thatcher, N. Wood, M. Cullen, B. Beare, M. Willett, F. Lemarié, E. Blayo, S. Malardel, P. Termonia, A. Gassmann, P. H. Lauritzen, H. Johansen, C. M. Zarzycki, K. Sakaguchi, R. Leung.

    Recent progress and review of Physics Dynamics Coupling in geophysical models, May 2016, working paper or preprint.

    https://hal.inria.fr/hal-01323768
  • 65W. Hoeffding.

    A class of statistics with asymptotically normal distribution, in: Ann. Math. Statistics, 1948, vol. 19, pp. 293–325.
  • 66A. Janon, T. Klein, A. Lagnoux-Renaudie, M. Nodet, C. Prieur.

    Asymptotic normality and efficiency of two Sobol index estimators, in: ESAIM: Probability and Statistics, October 2014, vol. 18, pp. 342-364. [ DOI : 10.1051/ps/2013040 ]

    https://hal.inria.fr/hal-00665048
  • 67A. Janon, M. Nodet, C. Prieur.

    Goal-oriented error estimation for reduced basis method, with application to certified sensitivity analysis.

    http://hal.archives-ouvertes.fr/hal-00721616
  • 68A. Janon, M. Nodet, C. Prieur.

    Certified reduced-basis solutions of viscous Burgers equation parametrized by initial and boundary values, in: ESAIM: Mathematical Modelling and Numerical Analysis, March 2013, vol. 47, no 2, pp. 317-348. [ DOI : 10.1051/m2an/2012029 ]

    http://hal.inria.fr/inria-00524727
  • 69A. Janon, M. Nodet, C. Prieur.

    Uncertainties assessment in global sensitivity indices estimation from metamodels, in: International Journal for Uncertainty Quantification, 2014, vol. 4, no 1, pp. 21-36. [ DOI : 10.1615/Int.J.UncertaintyQuantification.2012004291 ]

    https://hal.inria.fr/inria-00567977
  • 70A. Janon, M. Nodet, C. Prieur, C. Prieur.

    Global sensitivity analysis for the boundary control of an open channel, in: Mathematics of Control, Signals, and Systems, March 2016, vol. 28, no 1, pp. 6:1-27. [ DOI : 10.1007/s00498-015-0151-4 ]

    https://hal.archives-ouvertes.fr/hal-01065886
  • 71A. Janon, M. Nodet, C. Prieur, C. Prieur.

    Goal-oriented error estimation for fast approximations of nonlinear problems, GIPSA-lab, 2016, Rapport interne de GIPSA-lab.

    https://hal.archives-ouvertes.fr/hal-01290887
  • 72F. Lemarié, E. Blayo, L. Debreu.

    Analysis of ocean-atmosphere coupling algorithms : consistency and stability, in: Procedia Computer Science, 2015, vol. 51, pp. 2066–2075. [ DOI : 10.1016/j.procs.2015.05.473 ]

    https://hal.inria.fr/hal-01174132
  • 73F. Lemarié.

    Numerical modification of atmospheric models to include the feedback of oceanic currents on air-sea fluxes in ocean-atmosphere coupled models, Inria Grenoble - Rhône-Alpes ; Laboratoire Jean Kuntzmann ; Universite de Grenoble I - Joseph Fourier ; Inria, August 2015, no RT-0464.

    https://hal.inria.fr/hal-01184711
  • 74J. R. Leon, A. Samson.

    Hypoelliptic stochastic FitzHugh-Nagumo neuronal model: mixing, up-crossing and estimation of the spike rate, in: Annals of Applied Probability, 2017.

    https://hal.archives-ouvertes.fr/hal-01492590
  • 75S. Nanty, C. Helbert, A. Marrel, N. Pérot, C. Prieur.

    Uncertainy quantification for functional dependent random variables, 2014, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01075840
  • 76S. Nanty, C. Helbert, A. Marrel, N. Pérot, C. Prieur.

    Sampling, metamodelling and sensitivity analysis of numerical simulators with functional stochastic inputs, in: SIAM/ASA Journal on Uncertainty Quantification, May 2016, vol. 4, no 1, pp. 636-659. [ DOI : 10.1137/15M1033319 ]

    https://hal.archives-ouvertes.fr/hal-01187162
  • 77A. B. Owen.

    Sobol' indices and Shapley value, in: Journal on Uncertainty Quantification, 2014, vol. 2, pp. 245–251.
  • 78A. B. Owen, C. Prieur.

    On Shapley value for measuring importance of dependent inputs, in: SIAM/ASA Journal on Uncertainty Quantification, September 2017, vol. 51, no 1, pp. 986–1002. [ DOI : 10.1137/16M1097717 ]

    https://hal.archives-ouvertes.fr/hal-01379188
  • 79C. Pelletier, F. Lemarié, E. Blayo.

    A theoretical study of a simplified air-sea coupling problem including turbulent parameterizations, in: COUPLED PROBLEMS 2017 - VII International Conference on Computational Methods for Coupled Problems in Science and Engineering, Rhodes, Greece, M. Papadrakakis, E. Oñate, B. Schrefler (editors), International Center for Numerical Methods in Engineering (CIMNE) , June 2017, pp. 38-49.

    https://hal.archives-ouvertes.fr/hal-01659443
  • 80C. Pelletier, F. Lemarié, E. Blayo.

    Sensitivity analysis and metamodels for the bulk parameterization of turbulent air-sea fluxes, in: Quarterly Journal of the Royal Meteorological Society, December 2017. [ DOI : 10.1002/qj.3233 ]

    https://hal.inria.fr/hal-01663668
  • 81G. Salvadori, C. De Michele, F. Durante.

    On the return period and design in a multivariate framework, in: Hydrology and Earth System Sciences, 2011, vol. 15, no 11, pp. 3293–3305.
  • 82E. Song, B. L. Nelson, J. Staum.

    Shapley Effects for Global Sensitivity Analysis: Theory and Computation, Northwestern University, 2015.
  • 83P. Tencaliec, A.-C. Favre, C. Prieur, T. Mathevet.

    Reconstruction of missing daily streamflow data using dynamic regression models, in: Water Resources Research, December 2015, vol. 51, no 12, pp. 9447–9463. [ DOI : 10.1002/2015WR017399 ]

    https://hal.inria.fr/hal-01245238