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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

  • 1M. P. DAOU.

    Methodological development for model coupling with dimension heterogeneity. Validation on a realistic test-case, Université Grenoble Alpes, September 2016.

    https://tel.archives-ouvertes.fr/tel-01380084
  • 2L. 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

Articles in International Peer-Reviewed Journals

  • 3A. 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, 2016, pp. 1 - 19. [ DOI : 10.5194/gmd-2016-96 ]

    https://hal.inria.fr/hal-01406623
  • 4E. Blayo, D. Cherel, A. Rousseau.

    Towards optimized Schwarz methods for the Navier-Stokes equations, in: Journal of Scientific Computing, 2016, vol. 66, pp. 275–295.

    https://hal.inria.fr/hal-00982087
  • 5E. Blayo, A. Rousseau.

    About Interface Conditions for Coupling Hydrostatic and Nonhydrostatic Navier-Stokes Flows, in: Discrete and Continuous Dynamical Systems - Series S, 2016, vol. 9, pp. 1565–1574.

    https://hal.inria.fr/hal-01185255
  • 6P. Cattiaux, J. R. León, A. Pineda Centeno, C. Prieur.

    An overlook on statistical inference issues for stochastic dampinghamiltonian systems under the fluctuation-dissipation condition, in: Statistics, 2016. [ DOI : 10.1080/02331888.2016.1259807 ]

    https://hal.archives-ouvertes.fr/hal-01405427
  • 7L. Debreu, E. Neveu, E. Simon, F.-X. Le Dimet, A. Vidard.

    Multigrid solvers and multigrid preconditioners for the solution of variational data assimilation problems, in: Quarterly Journal of the Royal Meteorological Society, January 2016. [ DOI : 10.1002/qj.2676 ]

    https://hal.inria.fr/hal-01246349
  • 8G. Dollé, O. Duran, N. Feyeux, E. Frénod, M. Giacomini, C. Prud'Homme.

    Mathematical modeling and numerical simulation of a bioreactor landfill using Feel++, in: ESAIM: Proceedings and Surveys, 2016.

    https://hal.archives-ouvertes.fr/hal-01258643
  • 9L. Gilquin, L. A. Jiménez Rugama, E. Arnaud, F. J. Hickernell, H. Monod, C. Prieur.

    Iterative construction of replicated designs based on Sobol' sequences, in: Comptes Rendus Mathématique, December 2016. [ DOI : 10.1016/j.crma.2016.11.013 ]

    https://hal.inria.fr/hal-01349444
  • 10A. Janon, M. Nodet, C. Prieur.

    Goal-oriented error estimation for the reduced basis method, with application to sensitivity analysis, in: Journal of Scientific Computing, 2016, vol. 68, no 1, pp. 21-41.

    https://hal.archives-ouvertes.fr/hal-00721616
  • 11A. 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, 2016, vol. 28, no 1.

    https://hal.archives-ouvertes.fr/hal-01065886
  • 12A. Makris, C. Prieur, T. Vischel, G. Quantin, T. Lebel, R. Roca.

    Stochastic Tracking of Mesoscale Convective Systems: Evaluation in the West AfricanSahel, in: Stochastic Environmental Research and Risk Assessment, 2016, vol. 30, no 2, pp. 681-691, To appear in Stochastic Environmental Research and Risk Assessment. [ DOI : 10.1007/s00477-015-1102-9 ]

    https://hal.archives-ouvertes.fr/hal-01187153
  • 13S. 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, 2016, vol. 4, no 1, pp. 636-659.

    https://hal.archives-ouvertes.fr/hal-01187162
  • 14S. Nanty, C. Helbert, A. Marrel, N. Pérot, C. Prieur.

    Uncertainty quantification for functional dependent random variables, in: Computational Statistics, August 2016. [ DOI : 10.1007/s00180-016-0676-0 ]

    https://hal.archives-ouvertes.fr/hal-01075840
  • 15V. Oerder, F. Colas, V. Echevin, S. Masson, C. Hourdin, S. Jullien, G. Madec, F. Lemarié.

    Mesoscale SST – Wind Stress coupling in the Peru–Chile Current System: Which mechanisms drive its seasonal variability?, in: Climate Dynamics, January 2016, pp. 1-49. [ DOI : 10.1007/s00382-015-2965-7 ]

    https://hal.inria.fr/hal-01253181
  • 16L. Renault, J. Molemaker, J. C. Mcwilliams, A. Shchepetkin, F. Lemarié, D. Chelton, S. Illig, A. Hall.

    Modulation of Wind-Work by Oceanic Current Interaction with the Atmosphere, in: Journal of Physical Oceanography, 2016. [ DOI : 10.1175/JPO-D-15-0232.1 ]

    https://hal.inria.fr/hal-01295496
  • 17M. Saujot, M. DE LAPPARENT, E. Arnaud, E. Prados.

    Making Land Use - Transport models operational tools for planning: from a top-down to an end-user approach, in: Transport Policy, July 2016, vol. 49, pp. 20 - 29. [ DOI : 10.1016/j.tranpol.2016.03.005 ]

    https://hal.inria.fr/hal-01402863
  • 18V. Shutyaev, I. Gejadze, A. Vidard, F.-X. Le Dimet.

    Optimal solution error quantification in variational data assimilation involving imperfect models, in: International Journal of numerical methods in fluids, July 2016. [ DOI : 10.1002/fld.4266 ]

    https://hal.inria.fr/hal-01411666
  • 19V. Shutyaev, A. Vidard, F.-X. Le Dimet, I. Gejadze.

    On model error in variational data assimilation, in: Russian Journal of Numerical Analysis and Mathematical Modelling, January 2016, vol. 31, no 2, pp. 105-113. [ DOI : 10.1515/rnam-2016-0011 ]

    https://hal.inria.fr/hal-01309018
  • 20Y. Soufflet, P. Marchesiello, F. Lemarié, J. Jouanno, X. Capet, L. Debreu, R. Benshila.

    On effective resolution in ocean models, in: Ocean Modelling, February 2016, vol. 98, pp. 36–50. [ DOI : 10.1016/j.ocemod.2015.12.004 ]

    https://hal.inria.fr/hal-01250231

International Conferences with Proceedings

  • 21T. Capelle, P. Sturm, A. Vidard, B. Morton.

    Optimisation-Based Calibration and Model Selection for the Tranus Land Use Module, in: 14th World Conference on Transport Research, Shanghai, China, Transportation Research Procedia, Elsevier, July 2016.

    https://hal.inria.fr/hal-01396793
  • 22V. Chabot, A. Vidard, M. Nodet.

    Progressive assimilation of multiscale observations, in: ICCS 2016 - International Conference on Computational Science, Paris, France, November 2016.

    https://hal.inria.fr/hal-01411753

Conferences without Proceedings

  • 23É. Blayo, F. Lemarié, C. Pelletier.

    Toward improved ocean-atmosphere coupling algorithms, in: SIAM Conference on Mathematics of Planet Earth, Philadelphia, United States, September 2016.

    https://hal.inria.fr/hal-01413365
  • 24E. Kazantsev, F. Lemarié, É. Blayo.

    Lateral Boundary Conditions at the staircase-like boundary of ocean models, in: 6ème Colloque National d'Assimilation de données, Grenoble, France, November 2016.

    https://hal.inria.fr/hal-01415345
  • 25E. Kazantsev, F. Lemarié, É. Blayo.

    PACO : Vers une meilleure paramétrisation de la côte et des conditions limites dans les modèles d'océan, in: Journées Scientifiques LEFE/GMMC 2016, Toulon, France, Groupe Mission Mercator/Coriolis, June 2016.

    https://hal.inria.fr/hal-01416932
  • 26F. Lemarié, L. Debreu.

    A compact high-order coupled time and space discretization to represent vertical transport in oceanic models, in: Joint Numerical Sea Modelling Group Conference, Oslo, Norway, May 2016.

    https://hal.inria.fr/hal-01406629
  • 27F. Lemarié.

    Feasibility of a high-order semi-implicit vertical advection scheme in oceanic models, in: AGU Ocean Sciences Meeting, New Orleans, United States, February 2016.

    https://hal.inria.fr/hal-01406633
  • 28R. Pellerej, A. Vidard, F. Lemarié.

    Toward variational data assimilation for coupled models: first experiments on a diffusion problem, in: CARI 2016, Tunis, Tunisia, October 2016.

    https://hal.archives-ouvertes.fr/hal-01337743
  • 29A. Vidard, R. Pellerej, F. Lemarié.

    Improving coupled model solution mathematical consistency through data assimilation, in: International workshop on coupled data assimilation, Toulouse, France, October 2016.

    https://hal.inria.fr/hal-01411978

Scientific Books (or Scientific Book chapters)

  • 30M. Asch, M. Bocquet, M. Nodet.

    Data assimilation: methods, algorithms, and applications, Fundamentals of Algorithms, SIAM, 2016, xviii + 306 p.

    https://hal.inria.fr/hal-01402885
  • 31M. Nodet, A. Vidard.

    Variational methods, in: Handbook of Uncertainty Quantification, Springer International Publishing, 2016. [ DOI : 10.1007/978-3-319-11259-6_32-1 ]

    https://hal.inria.fr/hal-01251720

Internal Reports

Other Publications

  • 33V. Chabot, M. Nodet, A. Vidard.

    Taking into account correlated observation errors by progressive assimilation of multiscale information, December 2016, American Geophysical Union Fall Meeting, Poster.

    https://hal.inria.fr/hal-01402906
  • 34N. Feyeux, M. Nodet, A. Vidard.

    Optimal Transport for Data Assimilation, July 2016, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01342193
  • 35N. Feyeux, M. Nodet, A. Vidard.

    Optimal Transportation for Data Assimilation, July 2016, 5th International Symposium for Data Assimilation (ISDA 2016), Poster.

    https://hal.archives-ouvertes.fr/hal-01349637
  • 36L. 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
  • 37L. Gilquin, T. Capelle, E. Arnaud, C. Prieur.

    Sensitivity Analysis and Optimisation of a Land Use and Transport Integrated Model, March 2016, working paper or preprint.

    https://hal.inria.fr/hal-01291774
  • 38M. 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
  • 39L. A. Jiménez Rugama, L. Gilquin.

    Reliable error estimation for Sobol' indices, January 2017, working paper or preprint.

    https://hal.inria.fr/hal-01358067
  • 40E. Kazantsev.

    Parameterizing subgrid scale eddy effects in a shallow water model, December 2016, working paper or preprint. [ DOI : 10.1002/fld ]

    https://hal.inria.fr/hal-01413010
  • 41L. Li, F.-X. Le Dimet, J. Ma, A. Vidard.

    A level-set based image assimilation method: applications for predicting the movement of oil spills, November 2016, Submitted to IEEE Transactions on Geoscience and Remote Sensing.

    https://hal.inria.fr/hal-01411878
  • 42A. B. Owen, C. Prieur.

    On Shapley value for measuring importance of dependent inputs, October 2016, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01379188
  • 43R. Pellerej, A. Vidard, F. Lemarié.

    Toward variational data assimilation for coupled models: first experiments on a diffusion problem, July 2016, ISDA 2016, Poster.

    https://hal.archives-ouvertes.fr/hal-01412165
References in notes
  • 44F. Caron, P. Del Moral, A. Doucet, M. Pace.

    Particle approximation of the intensity measures of a spatial branching point process arising in multitarget tracking, in: SIAM J. Control Optim., 2011, vol. 49, no 4, pp. 1766–1792.

    http://dx.doi.org/10.1137/100788987
  • 45P. 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
  • 46P. 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
  • 47P. 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
  • 48M. 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.
  • 49G. Chastaing.

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

    https://tel.archives-ouvertes.fr/tel-00930229
  • 50G. 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
  • 51G. Chastaing, C. Prieur, F. Gamboa.

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

    http://hal.inria.fr/hal-00801628
  • 52A. 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
  • 53C. 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.
  • 54E. 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
  • 55E. 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
  • 56E. 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
  • 57F. Gamboa, A. Janon, T. Klein, A. Lagnoux-Renaudie, C. Prieur.

    Statistical inference for Sobol pick freeze Monte Carlo method, in: Statistics, 2016, vol. 50, no 4, pp. 881-902.

    https://hal.inria.fr/hal-00804668
  • 58L. 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
  • 59W. Hoeffding.

    A class of statistics with asymptotically normal distribution, in: Ann. Math. Statistics, 1948, vol. 19, pp. 293–325.
  • 60A. 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
  • 61A. 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
  • 62A. 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
  • 63F. 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
  • 64F. Lemarié, L. Debreu, E. Blayo.

    Optimal control of the convergence rate of Global-in-time Schwarz algorithms, in: Domain Decomposition Methods in Science and Engineering XX, R. Bank, M. Holst, O. Widlund, J. Xu (editors), volume 91 of Lecture Notes in Computational Science and Engineering, Springer-Verlag Berlin Heidelberg, 2013, pp. 599-606. [ DOI : 10.1007/978-3-642-35275-1_71 ]

    https://hal.archives-ouvertes.fr/hal-00661979
  • 65F. Lemarié, L. Debreu, E. Blayo.

    Toward an Optimized Global-in-Time Schwarz Algorithm for Diffusion Equations with Discontinuous and Spatially Variable Coefficients, Part 1: The Constant Coefficients Case, in: Electronic Transactions on Numerical Analysis, 2013, vol. 40, pp. 148-169.

    https://hal.archives-ouvertes.fr/hal-00661977
  • 66F. Lemarié, L. Debreu, E. Blayo.

    Toward an Optimized Global-in-Time Schwarz Algorithm for Diffusion Equations with Discontinuous and Spatially Variable Coefficients, Part 2: the Variable Coefficients Case, in: Electronic Transactions on Numerical Analysis, 2013, vol. 40, pp. 170-186.

    https://hal.archives-ouvertes.fr/hal-00661978
  • 67F. 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
  • 68F. Lemarié, P. Marchesiello, L. Debreu, E. Blayo.

    Sensitivity of Ocean-Atmosphere Coupled Models to the Coupling Method : Example of Tropical Cyclone Erica, Inria Grenoble ; Inria, December 2014, no RR-8651, 32 p.

    https://hal.inria.fr/hal-00872496
  • 69A. Makris, C. Prieur.

    Bayesian Multiple Hypothesis Tracking of Merging and Splitting Targets, in: IEEE Transactions on Geoscience and Remote Sensing, 2014, vol. 52, no 12, pp. 7684-7694. [ DOI : 10.1109/TGRS.2014.2316600 ]

    https://hal.inria.fr/hal-00919018
  • 70A. Owen.

    Variance components and generalized sobol' indices, 2012.

    http://arxiv.org/abs/1205.1774
  • 71A. B. Owen.

    Sobol' indices and Shapley value, in: Journal on Uncertainty Quantification, 2014, vol. 2, pp. 245–251.
  • 72A. Saltelli.

    Making best use of model evaluations to compute sensitivity indices, in: Computer Physics Communications, 2002, vol. 145, no 2, pp. 280 - 297. [ DOI : 10.1016/S0010-4655(02)00280-1 ]

    http://www.sciencedirect.com/science/article/pii/S0010465502002801
  • 73G. 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.
  • 74I. M. Sobol.

    Sensitivity estimates for nonlinear mathematical models, in: Math. Modeling Comput. Experiment, 1993, vol. 1, no 4, pp. 407–414 (1995).
  • 75E. Song, B. L. Nelson, J. Staum.

    Shapley Effects for Global Sensitivity Analysis: Theory and Computation, Northwestern University, 2015.
  • 76I. Souopgui, H. E. Ngodock, A. Vidard, F.-X. Le Dimet.

    Incremental projection approach of regularization for inverse problems, in: Applied Mathematics & Optimization, September 2015, 22 p. [ DOI : 10.1007/s00245-015-9315-3 ]

    https://hal.inria.fr/hal-01205235
  • 77I. Souopgui.

    Assimilation d'images pour les fluides géophysiques, Université Joseph-Fourier - Grenoble I, Oct 2010.
  • 78C. B. Storlie, T. C. M. Lee, J. Hannig, D. Nychka.

    Tracking of multiple merging and splitting targets: a statistical perspective, in: Statist. Sinica, 2009, vol. 19, no 1, pp. 1–31.
  • 79P. 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
  • 80J.-Y. Tissot, C. Prieur.

    A randomized Orthogonal Array-based procedure for the estimation of first- and second-order Sobol' indices, in: Journal of Statistical Computation and Simulation, 2014, pp. 1-24. [ DOI : 10.1080/00949655.2014.971799 ]

    https://hal.archives-ouvertes.fr/hal-00743964


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