Applied Visualization (AppVis)5 ECTS
(englische Bezeichnung: Applied Visualization)
(Prüfungsordnungsmodul: Applied Visualization)

Lehrveranstaltungen:

• • Applied Visualization
    (Vorlesung, 2 SWS, Tobias Günther, Do, 10:15 - 11:45, H4)
  • Tutorials to Applied Visualization
    (Übung, 2 SWS, Tobias Günther et al., Do, 14:15 - 15:45, 16:15 - 17:45, Zoom-Meeting)

Empfohlene Voraussetzungen:

Es wird empfohlen, folgende Module zu absolvieren, bevor dieses Modul belegt wird:

Algorithmen und Datenstrukturen (WS 2020/2021)

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 and vector 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.

This module covers the following topics:

• a review of scalar and vector calculus

• data structures and data acquisition techniques

• direct and indirect scalar field visualization

• geometry-based, feature-based and topology-based vector field visualization

• multi-variate data visualization

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There are voluntary exercises. Theoretical exercises concentrate on feature extraction from scalar and vector data, while programming exercises demonstrate the use of frameworks, such as the Visualization Tool Kit, to implement interactive scientific data visualizations.

The module is concluded with an electronic exam.

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
  

Literatur:

• M. Ward, G.G. Grinstein, D. Keim, Interactive Data Visualization: Foundations, Techniques, and Applications, Taylor & Francis, 2010

• AC. Telea, Data Visualization: Principles and Practice, AK Peters, 2008

• C.D. Hansen and C.R. Johnson, Visualization Handbook, Academic Press, 2004

• G.M. Nielson, H. Hagen, H.Müller, Scientific Visualization, IEEE Computer Society Press, Los Alamitos, 1997
  

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science)
   (Po-Vers. 2013 | TechFak | Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science) | Gesamtkonto | Wahlpflichtbereich Informatik | Wahlpflichtbereich Informatik | Applied Visualization)

Studien-/Prüfungsleistungen:

Applied Visualization (Prüfungsnummer: 37211)

(englischer Titel: Applied Visualisation)

Prüfungsleistung, elektronische Prüfung, Dauer (in Minuten): 90, benotet, 5.0 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

weitere Erläuterungen:
Klausur in elektronischer Form mit einem Anteil im Antwort-Wahl-Verfahren

Erstablegung: SS 2021, 1. Wdh.: WS 2021/2022

1. Prüfer:

Tobias Günther

Termin: 21.09.2021, 13:00 Uhr, Ort: Erlangen (91058), Gebbertstraße 123b, Ballspielhalle
Termin: 22.03.2022, 12:00 Uhr, Ort: Ballspielhalle, Gebbertstr. 123b, Erlangen