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4 results on '"Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow, Russian Federation"'

1. NEMO–ICB (v1.0): interactive icebergs in the NEMO ocean model globally configured at eddy-permitting resolution

Author

Adam T. Blaker, S.G. Alderson, Robert Marsh, Bablu Sinha, Vladimir Zalesny, Vladimir Ivchenko, Nikolaos Skliris, Andrew C. Coward, J. Le Sommer, Yevgeny Aksenov, N. Merino, Gurvan Madec, Grant R. Bigg, University of Southampton, National Oceanography Centre (NOC), Department of Geography [Sheffield], University of Sheffield [Sheffield], Nucleus for European Modeling of the Ocean (NEMO R&D ), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow, Russian Federation, Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Drift ice, geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, 010505 oceanography, lcsh:QE1-996.5, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], Antarctic sea ice, 01 natural sciences, Iceberg, Marine Sciences, lcsh:Geology, Oceanography, Fast ice, 13. Climate action, Sea ice thickness, [SDE]Environmental Sciences, Sea ice, Thermohaline circulation, 14. Life underwater, Sea ice concentration, Geology, 0105 earth and related environmental sciences
Abstract

An established iceberg module, ICB, is used interactively with the Nucleus for European Modelling of the Ocean (NEMO) ocean model in a new implementation, NEMO–ICB (v1.0). A 30-year hindcast (1976–2005) simulation with an eddy-permitting (0.25°) global configuration of NEMO–ICB is undertaken to evaluate the influence of icebergs on sea ice, hydrography, mixed layer depths (MLDs), and ocean currents, through comparison with a control simulation in which the equivalent iceberg mass flux is applied as coastal runoff, a common forcing in ocean models. In the Southern Hemisphere (SH), drift and melting of icebergs are in balance after around 5 years, whereas the equilibration timescale for the Northern Hemisphere (NH) is 15–20 years. Iceberg drift patterns, and Southern Ocean iceberg mass, compare favourably with available observations. Freshwater forcing due to iceberg melting is most pronounced very locally, in the coastal zone around much of Antarctica, where it often exceeds in magnitude and opposes the negative freshwater fluxes associated with sea ice freezing. However, at most locations in the polar Southern Ocean, the annual-mean freshwater flux due to icebergs, if present, is typically an order of magnitude smaller than the contribution of sea ice melting and precipitation. A notable exception is the southwest Atlantic sector of the Southern Ocean, where iceberg melting reaches around 50% of net precipitation over a large area. Including icebergs in place of coastal runoff, sea ice concentration and thickness are notably decreased at most locations around Antarctica, by up to ~ 20% in the eastern Weddell Sea, with more limited increases, of up to ~ 10% in the Bellingshausen Sea. Antarctic sea ice mass decreases by 2.9%, overall. As a consequence of changes in net freshwater forcing and sea ice, salinity and temperature distributions are also substantially altered. Surface salinity increases by ~ 0.1 psu around much of Antarctica, due to suppressed coastal runoff, with extensive freshening at depth, extending to the greatest depths in the polar Southern Ocean where discernible effects on both salinity and temperature reach 2500 m in the Weddell Sea by the last pentad of the simulation. Substantial physical and dynamical responses to icebergs, throughout the global ocean, are explained by rapid propagation of density anomalies from high-to-low latitudes. Complementary to the baseline model used here, three prototype modifications to NEMO–ICB are also introduced and discussed.

Published
2015
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2. Tensorizing Neural Networks

Author

Novikov, A., Podoprikhin, D., Anton Osokin, Vetrov, D., Osokin, Anton, Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow, Russian Federation, Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Laboratoire d'informatique de l'école normale supérieure (LIENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Statistical Machine Learning and Parsimony (SIERRA), Département d'informatique de l'École normale supérieure (DI-ENS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria), National Research University Higher School of Economics, Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria Paris-Rocquencourt, Vysšaja škola èkonomiki = National Research University Higher School of Economics [Moscow] (HSE), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Computer Science - Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Machine Learning (cs.LG)
Abstract

International audience; Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required by commonly used fully-connected layers, making it hard to use the models on low-end devices and stopping the further increase of the model size. In this paper we convert the dense weight matrices of the fully-connected layers to the Tensor Train format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved. In particular, for the Very Deep VGG networks we report the compression factor of the dense weight matrix of a fully-connected layer up to 200000 times leading to the compression factor of the whole network up to 7 times.

Published
2015

3. Evaluation of cloud and water vapor simulations in CMIP5 climate models Using NASA 'A-Train' satellite observations

Author

Jiang, J.H., Su, H., Zhai, C., Perun, V.S., Del Genio, A., Nazarenko, L.S., Donner, L.J., Horowitz, L., Seman, C., Cole, J., Gettelman, A., Ringer, M.A., Rotstayn, L., Jeffrey, S., Wu, T., Brient, F., Dufresne, J.-L., Kawai, H., Koshiro, T., Watanabe, M., Lécuyer, T.S., Volodin, E.M., Iversen, T., Drange, H., Mesquita, M.D.S., Read, W.G., Waters, J.W., Tian, B., Teixeira, J., Stephens, G.L., Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), NOAA Geophysical Fluid Dynamics Laboratory (GFDL), National Oceanic and Atmospheric Administration (NOAA), Canadian Centre for Climate Modelling and Analysis (CCCma), Environment and Climate Change Canada, National Center for Atmospheric Research [Boulder] (NCAR), Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], Commonwealth Scientific and Industrial Research Organisation, Clayton South, VIC, Australia, Queensland Climate Change Centre of Excellence, Dutton Park, QLD, Australia, Beijing Climate Centre (BCC), China Meteorological Administration (CMA), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École des Ponts ParisTech (ENPC)-École polytechnique (X)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Meteorological Research Institute [Tsukuba] (MRI), Japan Meteorological Agency (JMA), Model for Interdisciplinary Research on Climate, Atmospheric and Ocean Research Institute, University of Tokyo, Chiba, Japan, University of Wisconsin-Madison, Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow, Russian Federation, Norwegian Climate Centre, Norwegian Meteorological Institute [Oslo] (MET), Uni Research Ltd, Bjerknes Centre for Climate Research (BCCR), Department of Biological Sciences [Bergen] (BIO / UiB), University of Bergen (UiB)-University of Bergen (UiB), California Institute of Technology (CALTECH), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology
Abstract

International audience; [1] Using NASA's A-Train satellite measurements, we evaluate the accuracy of cloud water content (CWC) and water vapor mixing ratio (H2O) outputs from 19 climate models submitted to the Phase 5 of Coupled Model Intercomparison Project (CMIP5), and assess improvements relative to their counterparts for the earlier CMIP3. We find more than half of the models show improvements from CMIP3 to CMIP5 in simulating column-integrated cloud amount, while changes in water vapor simulation are insignificant. For the 19 CMIP5 models, the model spreads and their differences from the observations are larger in the upper troposphere (UT) than in the lower or middle troposphere (L/MT). The modeled mean CWCs over tropical oceans range from ~3% to ~15× of the observations in the UT and 40% to 2× of the observations in the L/MT. For modeled H2Os, the mean values over tropical oceans range from ~1% to 2× of the observations in the UT and within 10% of the observations in the L/MT. The spatial distributions of clouds at 215 hPa are relatively well-correlated with observations, noticeably better than those for the L/MT clouds. Although both water vapor and clouds are better simulated in the L/MT than in the UT, there is no apparent correlation between the model biases in clouds and water vapor. Numerical scores are used to compare different model performances in regards to spatial mean, variance and distribution of CWC and H2O over tropical oceans. Model performances at each pressure level are ranked according to the average of all the relevant scores for that level. © 2012. American Geophysical Union.

Published
2012
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4. On a Parallel Implementation of the Mortar Element Method

Author

Yves Achdou, Gassav S. Abdoulaev, Christophe Prud'Homme, Yuri A. Kuznetsov, Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA), Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow, Russian Federation, and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)

Subjects
Numerical Analysis, Mathematical optimization, Discretization, Iterative method, Preconditioner, Applied Mathematics, Domain decomposition methods, 010103 numerical & computational mathematics, 16. Peace & justice, 01 natural sciences, Mortar method, Finite element method, 010101 applied mathematics, Computational Mathematics, Modeling and Simulation, Saddle point, Applied mathematics, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], 65N55, 65M55, 0101 mathematics, Analysis, Mortar methods, Saddle, Mathematics
Abstract

International audience; We discuss a parallel implementation of the domain decomposition method based on the macro-hybrid formulation of a second order elliptic equation and on an approximation by the mortar element method. The discretization leads to an algebraic saddle- point problem. An iterative method with a block- diagonal preconditioner is used for solving the saddle- point problem. A parallel implementation of the method is emphasized. Finally the results of numerical experiments are presented.

Published
1999
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