Tracking features in embedded surfaces: Understanding extinction in turbulent combustion

Widanagamaachchi, Wathsala; Chen, Jackie; Klacansky, Pavol; Pascucci, Valerio; Kolla, Hemanth; Bhagatwala, Ankit; Bremer, Peer-Timo

doi:10.1109/ldav.2015.7348066

Cited by 13 publications

(5 citation statements)

References 23 publications

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“…These methods rely on the temporal and/or spatial coherence of attributes and location of the feature as it moves through time and space. Other methods [9,25,69,70] use a contour-based, merge-tree ideology to enable tracking of regions of interest in combustion simulations. Our finger tracking solution builds upon the combined success of these spatiotemporal feature graphs.…”

Section: Cfd Visualizationmentioning

confidence: 99%

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Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Luciani

Burks

Sugiyama³

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Visualization research often seeks designs that first establish an overview of the data, in accordance to the information seeking mantra: "Overview first, zoom and filter, then details on demand". However, in computational fluid dynamics (CFD), as well as in other domains, there are many situations where such a spatial overview is not relevant or practical for users, for example when the experts already have a good mental overview of the data, or when an analysis of a large overall structure may not be related to the specific, information-driven tasks of users. Using scientific workflow theory and, as a vehicle, the problem of viscous finger evolution, we advocate an alternative model that allows domain experts to explore features of interest first, then explore the context around those features, and finally move to a potentially unfamiliar summarization overview. In a model instantiation, we show how a computational back-end can identify and track over time low-level, small features, then be used to filter the context of those features while controlling the complexity of the visualization, and finally to summarize and compare simulations. We demonstrate the effectiveness of this approach with an online web-based exploration of a total volume of data approaching half a billion seven-dimensional data points, and report supportive feedback provided by domain experts with respect to both the instantiation and the theoretical model.

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Section: Cfd Visualizationmentioning

confidence: 99%

“…In order to minimize edge-crosses, we balance the trees using a heuristic similar to Widanagamaachchi et al's [69,70]. The heuristic begins with the fingers in the last timestep and recursively enumerates and sorts the children of each finger based on the latest timestep in which that finger appears.…”

Section: Finger Tracking and Temporal Context Calculationmentioning

confidence: 99%

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Luciani

Burks

Sugiyama³

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…Once features have been defined, and a methodology established to associate them in consecutive timesteps, the tracking representation is quite independent of whether geometrical or topological arguments have been used. Most often, graphs are used [45,64,92], such as Reeb graphs [23,91] and nested tracking graphs [47]. Many popular graph structures are accounted for in [89].…”

Section: Related Workmentioning

confidence: 99%

Lifted Wasserstein Matcher for Fast and Robust Topology Tracking

Soler

Plainchault

Conche

et al. 2018

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)

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This paper presents a robust and efficient method for tracking topological features in time-varying scalar data. Structures are tracked based on the optimal matching between persistence diagrams with respect to the Wasserstein metric. This fundamentally relies on solving the assignment problem, a special case of optimal transport, for all consecutive timesteps. Our approach relies on two main contributions. First, we revisit the seminal assignment algorithm by Kuhn and Munkres which we specifically adapt to the problem of matching persistence diagrams in an efficient way. Second, we propose an extension of the Wasserstein metric that significantly improves the geometrical stability of the matching of domain-embedded persistence pairs. We show that this geometrical lifting has the additional positive side-effect of improving the assignment matrix sparsity and therefore computing time. The global framework computes persistence diagrams and finds optimal matchings in parallel for every consecutive timestep. Critical trajectories are constructed by associating successively matched persistence pairs over time. Merging and splitting events are detected with a geometrical threshold in a post-processing stage. Extensive experiments on real-life datasets show that our matching approach is up to two orders of magnitude faster than the seminal Munkres algorithm. Moreover, compared to a modern approximation method, our approach provides competitive runtimes while guaranteeing exact results. We demonstrate the utility of our global framework by extracting critical point trajectories from various time-varying datasets and compare it to the existing methods based on associated overlaps of volumes. Robustness to noise and temporal resolution downsampling is empirically demonstrated.

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“…Most importantly, we obtain a feature track by means of a global analysis, i.e., by solving the shortest path problem on the entire graph. In a follow‐up article, Widanagamaachchi et al [WCK*15] locally adapt the threshold to produce more temporally cohesive feature tracks.…”

Section: Related Workmentioning

confidence: 99%

Global Feature Tracking and Similarity Estimation in Time‐Dependent Scalar Fields

Saikia

Weinkauf

2017

Computer Graphics Forum

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We present an algorithm for tracking regions in time‐dependent scalar fields that uses global knowledge from all time steps for determining the tracks. The regions are defined using merge trees, thereby representing a hierarchical segmentation of the data in each time step. The similarity of regions of two consecutive time steps is measured using their volumetric overlap and a histogram difference. The main ingredient of our method is a directed acyclic graph that records all relevant similarity information as follows: the regions of all time steps are the nodes of the graph, the edges represent possible short feature tracks between consecutive time steps, and the edge weights are given by the similarity of the connected regions. We compute a feature track as the global solution of a shortest path problem in the graph. We use these results to steer the – to the best of our knowledge – first algorithm for spatio‐temporal feature similarity estimation. Our algorithm works for 2D and 3D time‐dependent scalar fields. We compare our results to previous work, showcase its robustness to noise, and exemplify its utility using several real‐world data sets.

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Tracking features in embedded surfaces: Understanding extinction in turbulent combustion

Cited by 13 publications

References 23 publications

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Lifted Wasserstein Matcher for Fast and Robust Topology Tracking

Global Feature Tracking and Similarity Estimation in Time‐Dependent Scalar Fields

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