Visual Trends Analysis in Time-Varying Ensembles

Obermaier, Harald; Bensema, Kevin; Joy, Kenneth I.

doi:10.1109/tvcg.2015.2507592

Cited by 29 publications

(14 citation statements)

References 43 publications

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“…Traditional parameter space exploration methods first collect the simulation input and output pairs from ensemble runs, and perform parameter space exploration on the collected pairs. In the visualization field, to explore the parameter space of highdimensional ensemble data, researchers rely on visualization methods such as glyphs [11], matrices [12], line charts [13], parallel plots [2], [14], scatter plots [15]- [17], and radial plots [18]- [20]. The major limitation of these methods is the inability to analyze input parameters that have not been simulated.…”

Section: Parameter Space Explorationmentioning

confidence: 99%

GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations

Neng¹,

Fan²,

Wurster³

et al. 2022

Preprint

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We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is important for domain scientists to understand the influence of input parameters (e.g., wind stress) on the simulation output (e.g., temperature). The exploration requires scientists to exhaust the complicated parameter space by running a batch of computationally expensive simulations. Our approach improves the efficiency of parameter space exploration with a surrogate model that predicts the simulation outputs accurately and efficiently. Specifically, GNN-Surrogate predicts the output field with given simulation parameters so scientists can explore the simulation parameter space with visualizations from user-specified visual mappings. Moreover, our graph-based techniques are designed for unstructured meshes, making the exploration of simulation outputs on irregular grids efficient. For efficient training, we generate hierarchical graphs and use adaptive resolutions. We give quantitative and qualitative evaluations on the MPAS-Ocean simulation to demonstrate the effectiveness and efficiency of GNN-Surrogate. Source code is publicly available at https://github.com/trainsn/GNN-Surrogate.

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Section: Parameter Space Explorationmentioning

confidence: 99%

GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations

Neng¹,

Fan²,

Wurster³

et al. 2022

Preprint

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“…Ensemble visualization, an active and challenging visualization topic [18], is one of the target applications of our technique. Besides the aforementioned methods using space-filling curves [6,28], there are alternative techniques that use depth-based statistics [9,14,21,29], scatterplots and parallel coordinates [23], trend graphs and parallel coordinates [17], and a flexible linked-view system with a configurable collection of statistical representations [20]. Depth-based statistics is a fundamental building block for ensemble visualization.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Space-Filling Curves

Zhou

Johnson

Weiskopf

2021

IEEE Trans. Visual. Comput. Graphics

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We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization, including multivariate or comparative visualization, ensemble visualization of 2D and 3D data on regular grids, or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.

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“…One of the most recent comprehensive reviews of the ensemble visualization and visual analysis is offered by [49] , who summarized a wealth of past applications that utilize combined visualization techniques (e.g., vectors, color maps, glyphs, maps, and time-series) to simultaneously cover multiple facets and dimensions of the ensemble data. Examples of these applications include the visualization of ensemble uncertainty in (a) the spatial, ensemble, and multivariate dimensions [20] , [35] , [21] , (b) spatial and temporal dimensions [16] , [17] , [41] , and (c) temporal and ensemble dimensions [33] , [25] .…”

Section: Related Workmentioning

confidence: 99%