Scientific Visualization
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Faculteit  Science and Engineering 
Jaar  2018/19 
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  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 language (e.g. C, C++, Java, or C#), and integrate their implementation with a realworld, realtime data generation source. 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.  
Omschrijving  The aim of this course is to introduce students to the theory and practice of data visualization. At the end of the course, the students should be familiar with the aims and problems of data visualization, and have a good knowledge of the theory, principles, and methods which are frequently used in practice in the construction and use of data visualization applications. Moreover, the students should be able to design, implement, and customize a data visualization application of average complexity in order to get insight in a realworld dataset from one of the application domains addressed during the lecture. 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, volume data visualization, and domain modeling techniques. The techniques treated in the course are illustrated by means of several practical, handson, examples.  
Uren per week  
Onderwijsvorm 
Hoorcollege (LC), Practisch werk (PRC)
(Lectures 2 hours per week, lab 6 hours per week) 

Toetsvorm 
Practisch werk (PR), Schriftelijk tentamen (WE)
(opdracht en schriftelijk tentamen) 

Vaksoort  master  
Coördinator  prof. dr. A.C. Telea  
Docent(en)  prof. dr. A.C. Telea  
Verplichte literatuur 


Entreevoorwaarden   familiarity with a mainstream programming language (e.g. C, C++, Java, C#/.NET, Python, or similar). Matlab and JavaScript would not qualify.  having developed a small application in a mainstream programming language (over 500 lines or code)  familiarity with basic programming data structures and algorithms (arrays, trees, lists, sorting algorithms)  basic calculus and statistics and linear algebra notions (univariate function analysis, derivatives, gradients, histograms, normal distributions, matrixvector products, inner/outer products, matrix inverses)  familiarity with computer graphics is an advantage (polygon meshes, OpenGL) 

Opmerkingen  
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