Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures.

National Science Foundation [IIS-1045032, OCI-0904631, OCI-0906379, CCF-0702817]

National Science Foundation

King Abdullah University of Science and Technology (KAUST)

King Abdullah University of Science and Technology (KAUST) [KUS-C1-016-04]

US Department of Energy (DOE) by the University of Utah [DE-SC0001922, DE-AC52-07NA27344, DE-FC02-06ER25781]

US Department of Energy (DOE) by the University of Utah

Lawrence Livermore National Laboratory (LLNL) [DE-AC52-07NA27344]

Lawrence Livermore National Laboratory (LLNL)