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Scientific Visualization (SciVis)5 ECTS (englische Bezeichnung: Scientific Visualization)
Modulverantwortliche/r: Tobias Günther Lehrende:
Tobias Günther
Startsemester: |
SS 2022 | Dauer: |
1 Semester | Turnus: |
jährlich (SS) |
Präsenzzeit: |
60 Std. | Eigenstudium: |
90 Std. | Sprache: |
Englisch |
Lehrveranstaltungen:
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Scientific Visualization
(Vorlesung, 2 SWS, Tobias Günther, Di, 14:15 - 15:45, H4)
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Tutorials to Scientific Visualization
(Übung, 2 SWS, Tobias Günther et al., Do, 14:15 - 15:45, H4; Do, 10:15 - 11:45, Zoom-Meeting)
Empfohlene Voraussetzungen:
Es wird empfohlen, folgende Module zu absolvieren, bevor dieses Modul belegt wird:
Algorithmen und Datenstrukturen (WS 2021/2022)
Inhalt:
The amount of data, generated in the pursuit of scientific discovery, keeps rapidly increasing across all major scientific disciplines. How can we make sense of large, time-dependent, high-dimensional and multi-variate data? This lecture provides an introduction into scientific visualization. Throughout the course, we cover the fundamental perception basics needed to convey information accurately. After categorizing different data types based on their dimensionality, we dive deeper into specific techniques for scalar, vector and tensor valued data. To facilitate the discovery of patterns and to support the communication of findings, we further elaborate on data reduction, feature extraction, and interactive exploration.
The lecture covers the following topics:
a review of scalar and vector calculus
data structures and data types
direct and indirect scalar field visualization
geometry-based, feature-based and topology-based vector field visualization
glyph-based tensor field visualization
uncertainty and multi-variate data visualization
Lernziele und Kompetenzen:
Students are able to:
classify data and select appropriate visualization techniques
calculate differential properties of scalar and vector fields
identify features in scalar and vector-valued data
implement numerical extraction algorithms
learn the advantages and disadvantages of common visualization techniques
use perceptual basics to select appropriate visualization methods
explain and apply common interaction and data exploration paradigms
Studien-/Prüfungsleistungen:
Scientific Visualization (Prüfungsnummer: 37221)
- Prüfungsleistung, Klausur, Dauer (in Minuten): 90, benotet, 5 ECTS
- Anteil an der Berechnung der Modulnote: 100.0 %
- weitere Erläuterungen:
Klausur in elektronischer Form mit einem Anteil im Antwort-Wahl-Verfahren
- Prüfungssprache: Englisch
- Erstablegung: SS 2022, 1. Wdh.: WS 2022/2023
1. Prüfer: | Tobias Günther |
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UnivIS ist ein Produkt der Config eG, Buckenhof |
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