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Scientific Visualization
| Faculteit | Science and Engineering |
| Jaar | 2019/20 |
| Vakcode | INMSV-08 |
| 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. |
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| 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 implementation-level 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 above-mentioned 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 use-cases, 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.) |
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| 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.) |
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| Vaksoort | master | ||||||||||||
| Coördinator | prof. dr. J.B.T.M. Roerdink | ||||||||||||
| Docent(en) | prof. dr. J.B.T.M. Roerdink | ||||||||||||
| Verplichte literatuur |
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| Entreevoorwaarden | - Linear algebra - Calculus - Computer graphics - General-purpose programming (C,C++, Java, Python) |
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| Opmerkingen | |||||||||||||
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