Visual Analysis of Simulation Uncertainty Using Cost-Effective Sampling

Annie Preston, Yiran Li, Franz Sauer, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Studying large, complex simulations entails understanding their uncertainties. However, visualization tools that rapidly quantify simulation uncertainty may require precise tuning, give limited information, or struggle to disentangle uncertainty sources. We propose a fast, scalable regression-based approach that uses bootstrapping on small samples of simulation data to model the effect of uncertainty from discreteness. We test the approach on three types of simulations with unique sources of uncertainty: particles (dark matter), ensembles (ocean), and discretized flows (traffic). We create a visualization tool to facilitate this modeling, showing training data and predictions in real time. Scientists, who need to provide only modest supervision, can use our tool to quickly understand how initial conditions and parameterizations affect observable quantities, their uncertainties, and their agreement with experimental data. We show that our tool offers a speedup of several orders of magnitude over comparable uncertainty calculation approaches.

Original languageEnglish (US)
Title of host publication2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-11
Number of pages11
ISBN (Electronic)9781538668733
DOIs
StatePublished - Oct 1 2018
Event8th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2018 - Berlin, Germany
Duration: Oct 21 2018 → …

Publication series

Name2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018

Conference

Conference8th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2018
CountryGermany
CityBerlin
Period10/21/18 → …

Keywords

  • sampling
  • simulations
  • Uncertainty

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Media Technology
  • Modeling and Simulation

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  • Cite this

    Preston, A., Li, Y., Sauer, F., & Ma, K-L. (2018). Visual Analysis of Simulation Uncertainty Using Cost-Effective Sampling. In 2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018 (pp. 1-11). [8739182] (2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LDAV.2018.8739182