Uncertainty Visualization of 2D Morse Complex Ensembles Using Statistical Summary Maps

Tushar M. Athawale, Dan Maljovec, Lin Yan, Chris R. Johnson, Valerio Pascucci, Bei Wang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Morse complexes are gradient-based topological descriptors with close connections to Morse theory. They are widely applicable in scientific visualization as they serve as important abstractions for gaining insights into the topology of scalar fields. Data uncertainty inherent to scalar fields due to randomness in their acquisition and processing, however, limits our understanding of Morse complexes as structural abstractions. We, therefore, explore uncertainty visualization of an ensemble of 2D Morse complexes that arises from scalar fields coupled with data uncertainty. We propose several statistical summary maps as new entities for quantifying structural variations and visualizing positional uncertainties of Morse complexes in ensembles. Specifically, we introduce three types of statistical summary maps - the probabilistic map, the significance map, and the survival map - to characterize the uncertain behaviors of gradient flows. We demonstrate the utility of our proposed approach using wind, flow, and ocean eddy simulation datasets.

Original languageEnglish
Pages (from-to)1955-1966
Number of pages12
JournalIEEE Transactions on Visualization and Computer Graphics
Volume28
Issue number4
DOIs
StatePublished - Apr 1 2022

Funding

This work is supported in part by IIS-1910733,DBI-1661375, and IIS-1513616; the NIH Grants P41 GM103545-18 and R24 GM136986; the DOE Grant DE-FE0031880; and the Intel Graphics and Visualization Institutes of XeLLENCE.

Keywords

  • Morse complexes
  • topological data analysis
  • uncertainty visualization

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