Interactive Visual Analysis and Exploration of Injection Systems Simulations

Matković, Krešimir; Jelovic, Mario; Juric, J.; Konyha, Z.; Gračanin, Denis

doi:10.1109/visual.2005.1532821

Cited by 16 publications

(13 citation statements)

References 16 publications

(10 reference statements)

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“…For complex simulations such as diesel common injection systems, [25] proposes to use visualization tools to augment numerical optimization methods for analysis of simulation data and for investigating the effect of model parameter changes. [1] performs visualization-based interactive exploration of the parameter space guided by uncertainty in prediction of the underlying statistical model.…”

Section: Related Workmentioning

confidence: 99%

$$\mathrm{ND}^2\mathrm{AV}$$ ND 2 AV : N-dimensional data analysis and visualization analysis for the National Ignition Campaign

Bremer

Maljovec

Saha

et al. 2015

Comput. Visual Sci.

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One of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork Communicated by Gabriel Wittum. Electronic supplementary materialThe online version of this article (of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the ND 2 AV: Ndimensional data analysis and visualization framework, a user-friendly tool aimed at exploiting the intuition and current workflow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. ND 2 AV uses a modular design providing easy extensibility and customization for different applications. ND 2 AV is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries.

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Section: Related Workmentioning

confidence: 99%

$$\mathrm{ND}^2\mathrm{AV}$$ ND 2 AV : N-dimensional data analysis and visualization analysis for the National Ignition Campaign

Bremer

Maljovec

Saha

et al. 2015

Comput. Visual Sci.

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show abstract

“…In engineering [152] and climate research [86,166], so-called multi-run simulations are increasingly often performed to study the variability of a simulation model and to understand the model sensitivity to certain control parameters. According to Hamby [70], the goals of such a sensitivity analysis include the identification of model parameters that require additional research, which also reduces the output uncertainty; identifying control parameters that are correlated with the simulation output; or finding insignificant parameters that can be eliminated from the model [70].…”

Section: Multi-faceted Scientific Data: Characteristics and Challengesmentioning

confidence: 99%

“…The domain (i.e., the independent data dimensions) can be 2D or 3D space, time, but also independent input parameters to a simulation model. Multi-run data, for example, stems from a simulation which is repeated multiple times with varied control parameters, leading to a larger number of concurrent data volumes given for the same space/time [86,152]. With such data, the word multi-dimensional refers to the dimensionality of the independent variables, while multi-variate refers to the dependent variables of the data (compare to Wong and Bergeron [261]).…”

Section: Terminology and Structure Of The Chaptermentioning

confidence: 99%

“…The views can include 2D scatterplots, scatterplot matrices [11], parallel coordinates [91,102,172], function graph views [127,161], or histograms. Interesting data subsets are brushed [11] (selected) in the visual display, the related data items are instantly highlighted in the linked views (compare to the XmdvTool [248], Polaris/Tableau [217], or ComVis [127,152], for example). Logical combinations of brushes across multiple views support the specification of complex features, for instance, in a hierarchical feature definition language [52] or in conjunctive visual forms [251].…”

Section: Coordinated Multiple Viewsmentioning

confidence: 99%

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Hypothesis Generation in Climate Research with Interactive Visual Data Exploration

Kehrer

Ladstädter

Muigg

et al. 2008

IEEE Trans. Visual. Comput. Graphics

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One of the most prominent topics in climate research is the investigation, detection, and allocation of climate change. In this paper, we aim at identifying regions in the atmosphere (e.g., certain height layers) which can act as sensitive and robust indicators for climate change. We demonstrate how interactive visual data exploration of large amounts of multi-variate and time-dependent climate data enables the steered generation of promising hypotheses for subsequent statistical evaluation. The use of new visualization and interaction technology--in the context of a coordinated multiple views framework--allows not only to identify these promising hypotheses, but also to efficiently narrow down parameters that are required in the process of computational data analysis. Two datasets, namely an ECHAM5 climate model run and the ERA-40 reanalysis incorporating observational data, are investigated. Higher-order information such as linear trends or signal-to-noise ratio is derived and interactively explored in order to detect and explore those regions which react most sensitively to climate change. As one conclusion from this study, we identify an excellent potential for usefully generalizing our approach to other, similar application cases, as well.

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“…The combination of sorting and brushing makes the color lines view a very powerful technique for the visual exploration of families of function graphs. The color lines view is integrated in an interactive visualization software called ComVis [6] that provides conventional views like 2D and 3D scatter plots, parallel coordinates, histograms, as well as a curves view for displaying function graphs. ComVis provides features like composite brushing, sorting and linking that are very useful for visual analysis (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Color LinesView:AnApproach toVisualization of Families of Function Graphs

Matković

Gračanin

Konyha

et al. 2007

2007 11th International Conference Information Visualization (IV '07)

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Data sets often include information that can be represented as a mapping that describes how a dependent variable depends on an independent variable. Such a mapping, usually represented as a function graph, can be parameterized to provide a family of function graphs. The challenge is how to efficiently aggregate individual function graph views to represent the whole family and allow visual analysis and search for patterns. We propose a novel view, called the color lines view, which provides a two dimensional, rectangular view where each line represents a single function graph. The points on the line correspond to values of the independent variable. The point colors represent the value of the dependent variable. The lines, placed next to each other in parallel, show a family of function graphs. The color lines view offers sorting and brushing features which support visual analysis procedures that are difficult to perform with previously existing views.

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Interactive Visual Analysis and Exploration of Injection Systems Simulations

Cited by 16 publications

References 16 publications

$$\mathrm{ND}^2\mathrm{AV}$$ ND 2 AV : N-dimensional data analysis and visualization analysis for the National Ignition Campaign

$$\mathrm{ND}^2\mathrm{AV}$$ ND 2 AV : N-dimensional data analysis and visualization analysis for the National Ignition Campaign

Hypothesis Generation in Climate Research with Interactive Visual Data Exploration

Color LinesView:AnApproach toVisualization of Families of Function Graphs

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