Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
XML PDF e-pub
PDF e-Pub


Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]
M. R. El Amri.
Uncertainty and robustness analysis for models with functional inputs and outputs, Université Grenoble Alpes, April 2019.
https://tel.archives-ouvertes.fr/tel-02433324

Articles in International Peer-Reviewed Journals

[2]
T. 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, September 2019, vol. 77, pp. 101146:1-13. [ DOI : 10.1016/j.compenvurbsys.2017.04.009 ]
https://hal.inria.fr/hal-01519654
[3]
V. Chabot, M. Nodet, A. Vidard.
Multiscale Representation of Observation Error Statistics in Data Assimilation, in: Sensors, 2019, pp. 1-19, forthcoming.
https://hal.inria.fr/hal-02421699
[4]
X. Couvelard, F. Lemarié, G. Samson, J.-L. Redelsperger, F. Ardhuin, R. Benshila, G. Madec.
Development of a 2-way coupled ocean-wave model: assessment on a global NEMO(v3.6)-WW3(v6.02) coupled configuration, in: Geoscientific Model Development Discussions, August 2019, pp. 1-36. [ DOI : 10.5194/gmd-2019-189 ]
https://hal.inria.fr/hal-02267188
[5]
L. Debreu, N. K.-R. Kevlahan, P. Marchesiello.
Brinkman volume penalization for bathymetry in three-dimensional ocean models, in: Ocean Modelling, January 2020, vol. 145, pp. 1-13. [ DOI : 10.1016/j.ocemod.2019.101530 ]
https://hal.inria.fr/hal-02416084
[6]
J. Demange, L. Debreu, P. Marchesiello, F. Lemarié, E. Blayo, C. Eldred.
Stability analysis of split-explicit free surface ocean models: implication of the depth-independent barotropic mode approximation, in: Journal of Computational Physics, December 2019, vol. 398, no 108875, pp. 1-26. [ DOI : 10.1016/j.jcp.2019.108875 ]
https://hal.inria.fr/hal-01947706
[7]
M. R. El Amri, C. Helbert, O. Lepreux, M. Munoz Zuniga, C. Prieur, D. Sinoquet.
Data-driven stochastic inversion via functional quantization, in: Statistics and Computing, 2019, pp. 1-17, forthcoming. [ DOI : 10.1007/s11222-019-09888-8 ]
https://hal-ifp.archives-ouvertes.fr/hal-02291766
[8]
C. Eldred, T. Dubos, E. Kritsikis.
A Quasi-Hamiltonian Discretization of the Thermal Shallow Water Equations, in: Journal of Computational Physics, February 2019, vol. 379, pp. 1-31. [ DOI : 10.1016/j.jcp.2018.10.038 ]
https://hal.inria.fr/hal-01847698
[9]
C. Eldred, D. Le Roux.
Dispersion analysis of compatible Galerkin schemes on quadrilaterals for shallow water models, in: Journal of Computational Physics, June 2019, vol. 387, pp. 539-568. [ DOI : 10.1016/j.jcp.2019.02.009 ]
https://hal.archives-ouvertes.fr/hal-01916382
[10]
P. Etoré, C. Prieur, D. K. Pham, L. Li.
Global sensitivity analysis for models described by stochastic differential equations, in: Methodology and Computing in Applied Probability, July 2019, pp. 1-29, https://arxiv.org/abs/1811.08101. [ DOI : 10.1007/s11009-019-09732-6 ]
https://hal.archives-ouvertes.fr/hal-01926919
[11]
L. 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, July 2019, vol. 187, pp. 28-39. [ DOI : 10.1016/j.ress.2018.09.010 ]
https://hal.inria.fr/hal-01558915
[12]
F. 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, 2019, vol. 100, pp. ES109–ES115. [ DOI : 10.1175/BAMS-D-18-0303.1 ]
https://hal.inria.fr/hal-01939057
[13]
L. 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
[14]
A. W. Moore, M. J. Martin, S. Akella, H. G. Arango, M. Balmaseda, L. Bertino, S. Ciavatta, B. D. Cornuelle, J. Cummings, S. Frolov, P. Lermusiaux, P. Oddo, P. R. Oke, A. Storto, A. Teruzzi, A. Vidard, A. T. Weaver.
Synthesis of Ocean Observations Using Data Assimilation for Operational, Real-Time and Reanalysis Systems: A More Complete Picture of the State of the Ocean, in: Frontiers in Marine Science, March 2019, vol. 6, no 90, pp. 1-7. [ DOI : 10.3389/fmars.2019.00090 ]
https://hal.inria.fr/hal-02421672
[15]
C. Prieur, L. Viry, E. Blayo, J.-M. Brankart.
A global sensitivity analysis approach for marine biogeochemical modeling, in: Ocean Modelling, July 2019, vol. 139, no 101402, pp. 1-38. [ DOI : 10.1016/j.ocemod.2019.101402 ]
https://hal.inria.fr/hal-01952797
[16]
L. Renault, F. Lemarié, T. Arsouze.
On the implementation and consequences of the oceanic currents feedback in ocean-atmosphere coupled models, in: Ocean Modelling, September 2019, vol. 141, 101423 p. [ DOI : 10.1016/j.ocemod.2019.101423 ]
https://hal.inria.fr/hal-02190847
[17]
V. Shutyaev, F.-X. Le Dimet, E. Parmuzin.
Sensitivity of response functions in variational data assimilation for joint parameter and initial state estimation, in: Journal of Computational and Applied Mathematics, 2019, pp. 1-14. [ DOI : 10.1016/j.cam.2019.112368 ]
https://hal.inria.fr/hal-02431701
[18]
K. Smetana, O. Zahm, A. T. Patera.
Randomized residual-based error estimators for parametrized equations, in: SIAM Journal on Scientific Computing, March 2019, vol. 41, no 2, pp. A900-A926, https://arxiv.org/abs/1807.10489. [ DOI : 10.1137/18M120364X ]
https://hal.archives-ouvertes.fr/hal-01851462
[19]
P. Tencaliec, A.-C. Favre, P. Naveau, C. Prieur, G. Nicolet.
Flexible semiparametric Generalized Pareto modeling of the entire range of rainfall amount, in: Environmetrics, 2019, pp. 1-28, forthcoming. [ DOI : 10.1002/env.2582 ]
https://hal.inria.fr/hal-01709061

Invited Conferences

[20]
E. Blayo.
Vers une meilleure simulation du couplage océan-atmosphère, in: 2019 - Journées Tarantola : défis en géosciences, Paris, France, June 2019.
https://hal.inria.fr/hal-02415133
[21]
F. Lemarié.
An overview of the ocean-atmosphere coupling, in: 2019 - Physics-Dynamics Coupling in Earth System Models, Banff, Canada, October 2019.
https://hal.inria.fr/hal-02418164

Conferences without Proceedings

[22]
E. Blayo, F. Lemarié, C. Pelletier, S. Théry.
Toward an improved simulation of ocean-atmosphere interactions, in: 2019 - conférence Modélisation Océan-Atmosphère, Rennes, France, September 2019.
https://hal.inria.fr/hal-02415136
[23]
F. Lemarié, G. Samson, X. Couvelard, G. Madec, R. Bourdallé-Badie.
Recent developments in NEMO within the Albatross project, in: DRAKKAR 2019 - Drakkar annual workshop, Grenoble, France, January 2019.
https://hal.inria.fr/hal-02418218
[24]
F. Lemarié, G. Samson, J.-L. Redelsperger, G. Madec, H. Giordani, R. Bourdallé-Badie.
Toward an improved representation of air-sea interactions in high-resolution global oceanic forecasting systems, in: IMMERSE 2019 - IMMERSE Kick-Off Meeting, Grenoble, France, January 2019.
https://hal.inria.fr/hal-02418229

Other Publications

[25]
D. Bigoni, O. Zahm, A. Spantini, Y. Marzouk.
Greedy inference with layers of lazy maps, June 2019, https://arxiv.org/abs/1906.00031 - 15 pages, 24 figures.
https://hal.inria.fr/hal-02147706
[26]
M. Billaud Friess, A. Macherey, A. Nouy, C. Prieur.
Stochastic methods for solving high-dimensional partial differential equations, May 2019, https://arxiv.org/abs/1905.05423 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02140703
[27]
M. Brachet, J.-P. Chehab.
Fast and Stable Schemes for Phase Fields Models, September 2019, https://arxiv.org/abs/1909.13511 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02301006
[28]
L. Debreu, E. Kazantsev.
Optimal control of grids and schemes for the inertial gravity waves equation, January 2019, working paper or preprint.
https://hal.inria.fr/hal-01968678
[29]
M. Hilt, L. Roblou, C. Nguyen, P. Marchesiello, F. Lemarié, S. Jullien, F. Dumas, L. Debreu, X. Capet, L. Bordois, R. Benshila, F. Auclair.
Numerical Modelling of Hydraulic Control, Solitary Waves and Primary Instabilities in the Strait of Gibraltar, December 2019, working paper or preprint.
https://hal.inria.fr/hal-02418114
[30]
B. Iooss, C. Prieur.
Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications, November 2019, https://arxiv.org/abs/1707.01334 - working paper or preprint.
https://hal.inria.fr/hal-01556303
[31]
D. Le Roux, C. Eldred, M. Taylor.
Fourier analyses of continuous and discontinuous Galerkin methods of arbitrary degree of approximation, May 2019, working paper or preprint.
https://hal.inria.fr/hal-02125326
[32]
F. Lemarié, H. Burchard, K. Klingbeil, L. Debreu.
Challenges and prospects for dynamical cores of oceanic models across all scales, April 2019, PDEs on the sphere, Poster.
https://hal.inria.fr/hal-02418199
[33]
L. Li, A. Vidard, F.-X. Le Dimet, J. Ma.
Adaptive Image Assimilation for 2D Velocity Reconstruction, July 2019, AOGS 2019 - 16th Annual Meeting Asia Oceania Geosciences Society, Poster.
https://hal.inria.fr/hal-02263716
[34]
K. Smetana, O. Zahm.
Randomized residual-based error estimators for the Proper Generalized Decomposition approximation of parametrized problems, October 2019, https://arxiv.org/abs/1910.11837 - working paper or preprint. [ DOI : 10.11837 ]
https://hal.inria.fr/hal-02335617
[35]
V. Trappler, E. Arnaud, A. Vidard, L. Debreu.
Robust calibration of numerical models based on relative regret, February 2020, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02464780
[36]
O. Zahm, P. Constantine, C. Prieur, Y. Marzouk.
Gradient-based dimension reduction of multivariate vector-valued functions, November 2019, https://arxiv.org/abs/1801.07922 - working paper or preprint.
https://hal.inria.fr/hal-01701425
References in notes
[37]
A. 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
[38]
K. Bertin, N. Klutchnikoff, J. León, C. Prieur.
Adaptive density estimation on bounded domains under mixing conditions, December 2018, working paper or preprint.
https://hal.inria.fr/hal-01934913
[39]
P. 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
[40]
P. 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
[41]
P. 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
[42]
P. 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
[43]
M. R. El Amri, C. Helbert, O. Lepreux, M. Munoz Zuniga, C. Prieur, D. Sinoquet.
Data-driven stochastic inversion under functional uncertainties, February 2018, working paper or preprint.
https://hal.inria.fr/hal-01704189
[44]
F. Gamboa, A. Janon, T. Klein, A. Lagnoux, et al. .
Sensitivity analysis for multidimensional and functional outputs, in: Electronic Journal of Statistics, 2014, vol. 8, no 1, pp. 575–603.
[45]
L. 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
[46]
L. 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
[47]
D. P. Kingma, J. Ba.
Adam: A Method for Stochastic Optimization, 2014.
[48]
M. Lamboni, H. Monod, D. Makowski.
Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models, in: Reliability Engineering & System Safety, 2011, vol. 96, no 4, pp. 450–459.
[49]
F. 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
[50]
F. 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
[51]
F. Lemarié.
On the discretization of vertical diffusion in the turbulent surface and planetary boundary layers, in: PDC 2018 - 3rd workshop on Physics Dynamics Coupling, Reading, United Kingdom, July 2018.
https://hal.inria.fr/hal-01947691
[52]
J. 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
[53]
V. 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
[54]
A. B. Owen.
Sobol' indices and Shapley value, in: Journal on Uncertainty Quantification, 2014, vol. 2, pp. 245–251.
[55]
A. 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
[56]
C. Pelletier, F. Lemarié, É. 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
[57]
C. Pelletier, F. Lemarié, É. 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
[58]
E. Plischke, E. Borgonovo, C. L. Smith.
Global sensitivity measures from given data, in: European Journal of Operational Research, 2013, vol. 226, no 3, pp. 536–550.
[59]
E. Plischke.
An effective algorithm for computing global sensitivity indices (EASI), in: Reliability Engineering & System Safety, 2010, vol. 95, no 4, pp. 354–360.
[60]
E. Song, B. L. Nelson, J. Staum.
Shapley Effects for Global Sensitivity Analysis: Theory and Computation, Northwestern University, 2015.
[61]
S. Théry.
Algorithmes de Schwarz et conditions absorbantes pour le couplage océan-atmosphère, in: CANUM 2018 - 44e Congrès National d'Analyse Numérique, Cap d'Agde, France, May 2018.
https://hal.inria.fr/hal-01947885