Scientific Visualization / Visual Data Analytics (IN2026, IN8019)

Dr. Johannes Kehrer (Siemens AG)

Time, Place: Thursdays, 13:00-16:00 (on-campus + streaming)
Room: MW 0001, Gustav-Niemann-Hörsaal (5510.EG.001)
Begin: Oct. 19., 2023
Prerequisites Introduction to Informatics 1, Analysis, Linear Algebra

Lecture mode

  • The lectures will be both on-campus and streamed online via TUM-Live (livestream and video on demand).
  • Lecture slides will be made availabe via Moodle shortly before each lecture.
  • You can ask questions regarding the current lecture via the Moodle question forum
  • You do not have to be online during the official lecture hours (after the lecture, videos are available on demand via TUM-Live).

This course is intended for students in Informatics (Diploma/Master), Computational Science and Engineering, Computational Mechanics, Computational Methods in Applied Science and Engineering. It is given in English. The course consists of 3 lecture hours and 1 hour of free exercise, giving 5 ECTS.


The lecture gives an introduction to the fundamentals of visual data analytics. It discusses the different stages of the visualization pipeline and exemplifies application areas where visualization is paramount such as medicine, engineering, or computational fluid dynamics. Furthermore, it gives an overview of the many different data sources and addresses techniques that bring the initial data into a form that can be visualized. We then outline different strategies to map data onto a visual representation and discuss different visualization methods and algorithms. Finally, specific visualization fields are addressed such as volume and flow visualization, as well as the visual analysis of scientific data.


The students understand the basic visualization algorithms used by modern visualization software. They learn for which types of data these algorithms can be used, and they become aware of frequently used software systems supporting these algorithms. In the practical exercise, students are introduced to some available software systems, and they are supposed to work with these systems on their own initiative.

Course Topics

  • Introduction
  • Basics (visualization pipeline, data sources, data types)
  • Key application (medical imaging, computational fluid dynamics)
  • Data reconstruction, interpolation, triangulation
  • Filtering techniques
  • Basic data mapping techniques (color mapping, diagrams, glyphs, etc.)
  • Volume visualization (iso-surface rendering, direct volume rendering, etc.)
  • Vector field visualization (arrows, streamlines, vector field topology, etc.)
  • Visual analysis of scientific data