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

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

Dec 08, 2016

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) (Unlicensed), 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.