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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


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