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DC poleHodnotaJazyk
dc.contributor.authorPräger, Arne
dc.contributor.authorNsonga, Baldwin
dc.contributor.authorScheuermann, Gerik
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-01T08:21:37Z
dc.date.available2022-09-01T08:21:37Z
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 28-37.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49576
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectvizualizace prouděnícs
dc.subjectprůzkum datcs
dc.subjectinformační teoriecs
dc.subjectstatistická složitostcs
dc.subjectvizualizace turbulencecs
dc.titleVisualizing Statistical Complexity in 3D Turbulent Flows using a Robust Entropy Calculation Methoden
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedHighly resolved flow simulation data is becoming more common. These simulations frequently feature high tur- bulence with complex flow patterns. Finding these regions often requires expert knowledge, and in more complex cases, flow patterns of interest may remain hidden. The concept of statistical complexity was shown to be suitable to indicate regions of interest within flow data with limited prior knowledge. One way to determine the statistical complexity of flow fields is via the local vector field entropy and the correlation. In this work, we improve the method for calculating the Shannon entropy in vector fields. To this end, we introduce a robust entropy compu- tation that takes the scale of the corresponding regions into account. The improved method uses a novel way to determine the distributions required for the entropy calculation and is applicable to unstructured domains. We val- idate our method with analytic flow fields and apply it to fluid simulation data, visualizing the results via volume rendering. This work shows the applicability of our technique to highlight regions of interest in turbulent flows.en
dc.subject.translatedflow visualizationen
dc.subject.translateddata explorationen
dc.subject.translatedinformation theoryen
dc.subject.translatedstatistical complexityen
dc.subject.translatedturbulence visualizationen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.5
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2022: Full Papers Proceedings

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