Scientific Visualization
Faculteit  Science and Engineering 
Jaar  2019/20 
Vakcode  INMSV08 
Vaknaam  Scientific Visualization 
Niveau(s)  master 
Voertaal  Engels 
Periode  semester II a 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Scientific Visualization  
Leerdoelen  At the end of the course, the student is able to: 1. understand the basic principles of scientific visualization, and connect the theory to prior knowledge; understand the context provided by application domains of scientific visualization; understand the mathematical techniques needed in scientific visualization. 2. implement a number of basic scientific visualization techniques, working in a small team; use a suitable programming language and/or toolboxes for implementation. 3. report about the implementations, in terms of algorithms developed, experimental results, and critical analysis. 

Omschrijving  The aim of this course is to introduce students to the theory and practice of data visualization. After following this course, the students should have a good understanding of: scientific data representation issues (sampling, reconstruction, interpolation, representation in computer models); the structure and operation of the data visualization pipeline; and both theoretical and implementationlevel knowledge of the most frequently used algorithms for scalar, vector, and tensor data visualization. They should be able to select the appropriate algorithms, and algorithm settings, for solving a concrete scientific visualization problem for a given application domain and data source. On a practical side, the students should be able to design and implement the abovementioned algorithms in an efficient and effective manner in a major programming/scripting language. They should be able to explain the pro's and con's of the different algorithms for concrete usecases, and support their explanations with both theoretical and practical arguments. At the end, students should be familiar with the aims and problems of data visualization, and have a good knowledge of the theory, principles, and methods frequently used in practice in the construction and use of data visualization applications. The course addresses several technical topics, such as: data representation; different types of grids; data sampling, interpolation, and reconstruction; the concept of a dataset; the visualization pipeline. Several examples are treated, following the different types of visualization data: scalar visualization, vector visualization, tensor visualization.  
Uren per week  
Onderwijsvorm 
Hoorcollege (LC), Practisch werk (PRC)
(All lectures and lab sessions are mandatory.) 

Toetsvorm 
Schriftelijk tentamen (WE)
(The final grade F for this course is obtained as follows. Let P = mark practicals, E = mark written exam. If E<5 then F=E else F= (E+P)/2. For the resit, Final Grade = resit exam grade. Final grades are rounded to half integers, except for final grades between 5 and 6, which are rounded to integers. To pass the course, a final grade of at least 6 is required.) 

Vaksoort  master  
Coördinator  prof. dr. J.B.T.M. Roerdink  
Docent(en)  prof. dr. J.B.T.M. Roerdink  
Verplichte literatuur 


Entreevoorwaarden   Linear algebra  Calculus  Computer graphics  Generalpurpose programming (C,C++, Java, Python) 

Opmerkingen  
Opgenomen in 