SIAM GS 2017

SIAM Conference on Mathematical and Computational Issues in the Geosciences
September 11—14, 2017 • Erlangen, Germany

CONFERENCE details

Scientific program

Sunday, 10 September
17:00–21:00: Icebreaker at the Orangery, Erlangen

Monday, 11 September–Thursday, 14 September
from 09:00–18:00: Daily conference program

Invited Speakers
Alexandre Ern, ParisTech, France
Christiane Jablonowski, University of Michigan, USA
Kundan Kumar, University of Bergen, Norway: SIAG/Geosciences Early Career Prize
Malgorzata Peszynska, Oregon State University, USA
Siegfried Raasch, University of Hannover, Germany
Juan M. Restrepo, Oregon State University, USA: SIAG/Geosciences Career Prize
Hamdi Tchelepi, Stanford University, USA
Nils Wedi, ECMWF, United Kingdom

Themes and Topics
• Atmospheric modeling
• Biosphere modeling
• Cryosphere and hydrosphere modeling
• Lithosphere and pedosphere modeling
• Ocean/estuarine/nearshore modeling
• Climate and weather dynamics

Cross-sectional Application Fields
• Carbon sequestration
• Geothermal energy
• Hydrogen or compressed air storage
• Methane hydrates
• Nuclear waste disposal
• Oil exploration and improved oil recovery
• Solar and wind energy
• Thermo-chemical storage
• Shale gas and oil
• Changing climate
• Global carbon cycle
• Natural hazards (earthquakes, tsunamis, hurricanes, etc.)
• Seismic wave and rupture propagation

Mathematical Methods
• Mathematical modeling
          • Fundamental modeling and scale transitions
          • Advanced theories and non-standard models
          • Flow, reactions, transport and mechanical effects in complex media

• Computational and mathematical methods
          • Algorithms and discretization methods
          • Solution methods for coupled systems
          • Error analysis and estimation
          • Linear and nonlinear solvers
          • Multiscale, upscaling, and model-reduction methods
          • Mathematical analysis methods
          • Optimization methods
          • Stochastic methods and parameterization
          • High-performance computing
          • Inverse problems
          • Geophysical turbulence

• Model data and parameters
          • Data assimilation
          • Data classification, big data
          • Model uncertainty
          • Model calibration
          • Validation and verification
          • Experiments and observations design