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DC poleHodnotaJazyk
dc.contributor.authorMolchanov, Vladimir
dc.contributor.authorLinsen, Lars
dc.contributor.editorSkala, Václav
dc.date.accessioned2017-10-10T06:31:58Z
dc.date.available2017-10-10T06:31:58Z
dc.date.issued2014
dc.identifier.citationWSCG 2014: communication papers proceedings: 21st International Conference in Central Europeon Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 39-48.en
dc.identifier.isbn978-80-86943-71-8
dc.identifier.uriwscg.zcu.cz/WSCG2014/!!_2014-WSCG-Communication.pdf
dc.identifier.urihttp://hdl.handle.net/11025/26376
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2014: communication papers proceedingsen
dc.rights@ Václav Skala - UNION Agencycs
dc.subjectčasově různé údajecs
dc.subjectvíce polícs
dc.subjectmultimodální datacs
dc.subjectvícerozměrová datacs
dc.subjectdata skalárních polícs
dc.subjectsnížení dimenzecs
dc.titleVisual exploration of patterns in multi-run time-varying multi-field simulation data using projected viewsen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn the fields of science and engineering, it is common to run hundreds of simulations to investigate the dependence of the modeled process on various simulation and input the parameters. We propose a comprehensive approach for the visual analysis of such multi-run data to detect patterns and outliers. We use dimensionality reduction algorithms to generate a visual representation that exhibits the distribution of the simulation results under varying parameter settings. Each field (or even multi-field) of every time step and every simulation run is represented as a point in a 2D space, where the 2D layout conveys similarity of the scalar fields. Points corresponding to consecutive time steps of one run are connected by line segments, such that each simulation run is represented as a polyline. Consequently, the multi-run data are visually encoded as a set of polylines. Variations of hue, saturation, opacity, and shape allow for distinguishing groups of simulations and depicting various characteristics of runs. The user can interactively change these settings, while further interaction mechanisms allow for selection, refinement, zooming, requesting textual information, and brushing and linking to coordinated (or embedded) views of physical and attribute space visualizations. We apply our approach to two applications with significantly different data structure: a multi-run climate simulation over a 2D regular grid and a multi-run binary star evolution simulation with unstructured 3D particles evolving over time. We demonstrate the contribution and impact of our visualization method for the interactive visual analysis of the multi-run data by identifying meaningful groups of simulations, detecting global patterns, and finding interesting outliers.en
dc.subject.translatedtime-varying dataen
dc.subject.translatedmulti-fielden
dc.subject.translatedmulti-modal dataen
dc.subject.translatedmulti-variate dataen
dc.subject.translatedscalar field dataen
dc.subject.translateddimensionality reductionen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2014: Communication Papers Proceedings

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