Sponsored by the SIAM Activity Group on the Geosciences.

From science to public policy, there is a growing interest in modeling and simulation of geosystems and their applications. Example applications include petroleum exploration and recovery, underground waste disposal and cleanup of hazardous waste, earthquake prediction, weather prediction, and global climate change. Such modeling is fundamentally interdisciplinary; physical and mathematical modeling at appropriate scales, physical experiments, mathematical theory, probability and statistics, numerical approximations, and large-scale computational algorithms all have important roles to play.

This conference facilitates communication between scientists of varying backgrounds and work environments facing similar issues in different fields. Such interactions are needed for meaningful progress in understanding and predicting complex physical phenomena in the geosciences.

**Mathematical Models in the Geosciences**

**Atmospheric modeling**: Meteorology, Urban climate**Biosphere modeling****Cryosphere and hydrosphere modeling**: Glaciology, Hydrology and limnology, Oceanography, Sea ice and ice cap**Lithosphere and pedosphere modeling**: Geochemistry, Geology and geophysics, Plate tectonics and earth dynamics, Soil science, Volcanoes and earthquakes**Climate**: Climate system, Paleoclimate, Climate change**Energy resource modeling**: Carbon sequestration, Geothermal energy, Hydrogen or compressed air storage, Methane hydrates, Nuclear waste disposal, Oil exploration and improved oil recovery, Shale gas and oil, Solar and wind energy, Thermo-chemical storage

**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, Machine Learning, High-performance computing**Model data and parameters**: Data assimilation, Data classification, big data, Model uncertainty, Model calibration, inverse problems, uncertainty reduction, Validation and verification, Design of experiments

**Clint Dawson**University of Texas at Austin, U.S.

SIAM and the Conference Organizing Committee wish to extend their thanks and appreciation to the U.S. National Science Foundation and DOE Office of Advanced Scientific Computing Research for their support of this conference.

SIAM invites you to show support of this meeting through sponsorship opportunities ranging from support of receptions, audio-video needs, to awards for student travel, and more.

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