#13 High-dimensional big data exploration for model tuning and evaluation

1.Poster Title	High-dimensional big data exploration for model tuning and evaluation
2.Authors	Hui Wan, Jonas Lukasczyk, David Rogers, Phil Rasch (pnl.gov), Ross Maciejewski, Hans Hagen
3.Group	Atmosphere, Workflow
4.Experiment	N/A
5.Poster Category	Future Directions
6.Submission Type	Poster (and Lightning Talk)
7.Poster Link	13_HuiWan_Poster_HighDimensional
8.Lightning Talk Slide	HuiWan_one-slider_3_HighDimensional.pdf

Abstract

Model tuning and evaluation, including uncertainty quantification exercises like the parametric uncertainty analysis, are challenging and time-consuming tasks. The many simulations and the large number of output variables result in a high-dimensional space that needs to be explored in a timely manner. The prototype of a web-based interactive ensemble viewer is presented in this poster. The new tool can substantially reduce the need for tedious scripting and facilitate the evaluation of model results by large groups of modelers. We think further development and possible incorporation of the tool in the ACME analysis tool suite will be useful to the project, and invite people to stop by and learn about the new viewer.