2011-12 Program on Uncertainty Quantification: Geosciences
Geoscience applications: Many problems in geoscience and environmental engineering are described by computationally expensive models. Computational time is arguably the main obstacle to doing rigorous statistical analysis of uncertainty. Further, multiple types of uncertainty need to be incorporated, including data error, model error, parameter error, randomness in model input (static and dynamic). The type of issues call for new algorithms. Specific examples of problems requiring careful uncertainty quantification in this field include
* determination of spatial distribution of geologic materials in the subsurface based on sound wave and/or radar (many spatial points, low accuracy),
* determination of location of oil reservoirs or underground water based on exploratory drilling (few spatial points, high accuracy),
* forecast of contaminant transport,
* modeling of hurricanes, volcanos and tsunamis.
Organizers: Omar Ghattas (Univ. of Texas-Austin), Christine Shoemaker (Cornell University), Daniel Tartakovsky (University of California - San Diego)