Making Sense of Scientific Simulation Ensembles

Authors: Mai Dahshan (Virginia Tech), Nicholas Polys (Virginia Tech)

Abstract: Scientists run many simulations with varying initial conditions, known as "ensembles", to understand the influence and relationships among multiple parameters or ensemble members. Most of the ensemble visualization and analysis approaches and techniques focus on analyzing the relationships between either the ensemble members or output parameter space while neglecting the effect of input parameters and humans in the analysis loop. Therefore, we developed an approach to the visual analysis of scientific data that merges human expertise and intuition with machine learning and statistics allowing scientists to explore, search, filter, and make sense of their high dimensional ensemble. Our tool, "GLEE" (Graphically-Linked Ensemble Explorer), is an interactive visualization tool that consists of three visual views: Ensemble View, Parameter View, and Statistical View. Each view offers different functionality for exploration and interoperation of the relations and correlations between different runs, a subset of runs, and input and output parameters.

Best Poster Finalist (BP): no

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