MAT-82306 Scientific Visualization, 4 cr
Additional information
No implementations in 2015-2016. Lectured every second calendar year (during academic years starting with an even number). Implementation announcements and news are available at the teaching home page of the TUT Department of Mathematics, Intelligent Information Systems Laboratory.
Suitable for postgraduate studies
Will not be lectured year 2015-2016
Person responsible
Ossi Nykänen
-->Requirements
The grade is based on the assignments and the final exam.
Learning Outcomes
Scientific visualization is about rigorously and semi-automatically processing and projecting data in terms of visual and interactive representations with analysis or application utility. After actively studying the course, the student understands the basic concepts, methods, and applications related to scientific (data) visualization. Further, the student knows a wide range of different specific visualization techniques, can implement certain kinds of simple scientific visualizations using Matlab and Paraview, and has basic understanding how to critically evaluate the resulting visualizations.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Fundamental scientific (data) visualization concepts (incl. interpolation, artefacts, use of symmetry, etc.); Visualization targets and benefits; Dataset and grid concepts | Identification of the "nearby tasks" (incl. data pre-processing, scientific computing, and planning for perception) | |
| 2. | Examples of various scientific visualizations techniques by dataset type; Visualization process and visualization pipeline | Classification of visualization software; Hands-on skills using the selected applications (Matlab, ParaView); Interaction and feedback mechanisms | |
| 3. | Data structures and techniques related to basic scalar visualization techniques (colormaps, contours, isosurfaces, elevation plots); Data structures and techniques related to basic vector visualization techniques (vector glyphs, vector color coding, displacement plots, stream objects); | Selected, more specific scientific visualization areas (tensor, image, and volume visualization) | |
| 4. | Information visualization basics and application examples (incl. table lens, graph, tree, and parallel coordinate visualisation techniques) | Relationship to general-purpose data processing pipelines and visualization systems |
Instructions for students on how to achieve the learning outcomes
Working actively with the exercises and the assignments, hands-on and throughout the course, is essential. (Simply "listening" and "reading" is not enough.)
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Additional information about prerequisites
Hands-on programming/scripting skills are recommended. Basic understanding on the rudimentary (numerical) scientific computation methods is helpful, but strictly speaking not needed in every application.
Correspondence of content
There is no equivalence with any other courses