Scientific Visualization
Module IN8019
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Basic Information
IN8019 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 120 h | 45 h | 4 CP |
Content, Learning Outcome and Preconditions
Content
Visualization pipeline (data acquisition, filtering, display), information visualization vs. scientific visualization, grids and grid construction (Delaunay triangulation), interpolation in grids (inverse distance weighting, radial basis functions), discretization aspects, visualization for scalar fields (color coding, iso-contours and iso-surfaces, volume rendering, vector field visualization (particle-based visualization).
Learning Outcome
After successful completion of the module, the students have gained advanced knwowledge concerning the visualization pipeline, ranging from data acquisition to the final image of this data. This includes knowledge about the application specific data representations, data interpolation and approximation techniques for discrete data sets, data filtering techniques like convolution, as well as the final mapping stage to generate a renderable representation from the data. The students know the methods which are used in scientific visualiztion to graphically depict 2D and 3D scalar and vector fields, including isocontouring, direct volume rendering and flow visualization. They can analyse and categorize availaible techniques in terms of quality, efficiency, and suitability for a particular data type, and they can model and develop new approaches considering application-specific requirements.
Preconditions
None
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
WS 2022/3
WS 2021/2
WS 2020/1
WS 2019/20
WS 2018/9
WS 2017/8
WS 2016/7
WS 2015/6
WS 2014/5
WS 2012/3
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VI | 4 | Visual Data Analytics (IN2026, IN8019) | Kehrer, J. Weitz, S. Westermann, R. |
Thu, 13:00–16:00, virtuell |
eLearning documents |
Learning and Teaching Methods
The modul consists of the lecture, where the lecturer conveys to the students the area-specific knowledge, points towards relevant articles and ecourages the students to read and put into relation the presented approaches, and gives examples demonstrating the application of these approaches. In some online demonstration the lecturer introduces state-of-the-art tools for scientific visualization.
Media
Powerpoint course slides, white board exercises, online tutorials and demonstrations
Literature
Schumann, Müller: Visualisierung - Grundlagen und allgemeine Methoden, Springer Verlag
C. Hansen, C. Johnson (Ed.): The handbook of Visualization, Academic Press
C. Hansen, C. Johnson (Ed.): The handbook of Visualization, Academic Press
Module Exam
Description of exams and course work
The exam takes the form of a written test of 75 minutes.
The students demonstrate that they can answer questions concerning the theoretical and methodological foundations of scientific visualization. They know important application domains where visualization methods are used, and they are familiar with the application-specific data modalities a visualization person is confronted with. They also demonstrate the ability to apply the learned concepts and methods, such as the color mapping, iso-contouring or particle-tracing, to derive solutions for specific visualization problems. The exam captures all content discussed in the lecture.
The students demonstrate that they can answer questions concerning the theoretical and methodological foundations of scientific visualization. They know important application domains where visualization methods are used, and they are familiar with the application-specific data modalities a visualization person is confronted with. They also demonstrate the ability to apply the learned concepts and methods, such as the color mapping, iso-contouring or particle-tracing, to derive solutions for specific visualization problems. The exam captures all content discussed in the lecture.
Exam Repetition
The exam may be repeated at the end of the semester.