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1.Poster TitleHigh-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.GroupAtmosphere, Workflow
4.ExperimentN/A
5.Poster CategoryFuture Directions
6.Submission TypePoster (and Lightning Talk)
7.Poster Link13_HuiWan_Poster_HighDimensional
8.Lightning Talk SlideHuiWan_one-slider_3_HighDimensional.pdf

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name13_HuiWan_Poster_HighDimensional.pdf
page2016-06-07 ACME Project All-Hands Meeting Posters
height400

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.