SIAM Conference on Mathematical and Computational Issues in the Geosciences (GS15)

June 29-July 2, 2015
Stanford University, Stanford, California USA

Conference Co-Chairs
Knut-Andreas Lie, SINTEF, Norway
Iuliu Sorin Pop, Eindhoven University of Technology, The Netherlands
Margot Gerritsen, Stanford University, USA

Carol S. Woodward, Lawrence Livermore National Laboratory, USA
Hans Kaper, Argonne National Laboratory, USA
Ibrahim Hoteit, King Abdullah University of Science and Technology, Saudi Arabia
Jennifer Suckale, Stanford University, USA
Laurent Demanet, Massachusetts Institute of Technology, USA
Marc Hesse, University of Texas At Austin, USA
Robert Higdon, Oregon State University, USA
Scott King, Virginia Tech, USA
Ulisses Mello, IBM Research - Brazil

Conference Themes

Mathematical Models in the Geosciences
  1. Atmospheric modeling
    1. Meteorology and hypermeteorology
    2. Monsoons
    3. Urban climate
  2. Biosphere modeling
  3. Cryosphere and hydrosphere modeling
    1. Glaciology
    2. Hydrology and limnology
    3. Oceanography
    4. Sea ice and ice cap
  4. Lithosphere and pedosphere modeling
    1. Geochemistry
    2. Geology
    3. Geophysics
    4. Plate tectonics and earth dynamics
    5. Soil science
    6. Volcanoes and earthquakes
  5. Climate
    1. Climate system
    2. Paleoclimate
    3. Climate change
  6. Energy resource modeling
    1. Carbon sequestration
    2. Geothermal energy
    3. Hydrogen or compressed air storage
    4. Methane hydrates
    5. Nuclear waste disposal
    6. Oil exploration and improved oil recovery
    7. Shale gas and oil
    8. Solar and wind energy
    9. Thermo-chemical storage
Mathematical Methods
  1. Mathematical modeling
    1. Fundamental modeling and scale transitions 
    2. Advanced theories and non-standard models
    3. Flow, reactions, transport and mechanical effects in complex media
  2. Computational and mathematical methods
    1. Algorithms and discretization methods
    2. Solution methods for coupled systems
    3. Error analysis and estimation
    4. Linear and nonlinear solvers
    5. Multiscale, upscaling, and model-reduction methods
    6. Mathematical analysis methods 
    7. Optimization methods
    8. Stochastic methods
    9. High-performance computing
  3. Model data and parameters
    1. Data assimilation
    2. Data classification, big data
    3. Model uncertainty
    4. Model calibration, inverse problems, uncertainty reduction
    5. Validation and verification
    6. Experiments and observations design

 

 The call for papers will be posted August 2014.

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