Knowledge extraction via comparison of complex computational models to massive data sets

July 29 – July 31, 2013

Advances in computation have enormously improved our abilities to model complex processes in science, engineering and the social sciences. In parallel, experimental observations have grown in size and complexity beyond what could have been imagined a few decades ago. Gaining knowledge and insight from these efforts requires rigorous comparison of models and data. The ever increasing sophistication of the models along with the size and detail of the heterogeneous data sets demands commensurate advances in the processes and practices of data analysis.

This workshop, co-sponsored by SAMSI and the MADAI collaboration, will introduce a broader base of domain scientists to statistical and visualization tools that facilitate knowledge extraction via complex model to data comparisons. The workshop will also provide opportunities for the Statistical Science community to learn about recent developments in complex modeling and computer experiments as well as engage in new collaborative ventures. The hands-on tutorials will showcase a modular visualization platform (based on Paraview) that allows for advanced visualization of complex model dynamics as well as statistical analysis tools. The statistical tools are based on Gaussian process surrogate models for rapid exploration of a model’s parameter space.