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260091 VO Scientific Computing (2026S)
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Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Details
Language: German
Lecturers
- Georg Kresse
- Alessandro Coretti
- Serafin Iwaniewicz (Student Tutor)
Classes (iCal) - next class is marked with N
- Tuesday 03.03. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 10.03. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 17.03. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 24.03. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 14.04. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 21.04. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 28.04. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 05.05. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 12.05. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 19.05. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 26.05. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 02.06. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 09.06. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 16.06. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
- Tuesday 23.06. 10:45 - 12:15 Josef-Stefan-Hörsaal, Boltzmanngasse 5, 3. Stk., 1090 Wien
Information
Aims, contents and method of the course
This course introduces the fundamental methods of scientific computing, with a particular emphasis on applications in theoretical physics. Scientific computing is an inherently interdisciplinary field, lying at the intersection of numerical analysis, computer science, and the natural sciences. In contemporary research, numerical simulations play a central role, enabling the investigation of complex mathematical models of physical systems where experiments may be prohibitively expensive, technically unfeasible, or entirely impossible. Insights gained from such simulations often guide theoretical developments and suggest new directions for analytical research.The primary aim of the course is to equip students with essential numerical methods for the analysis and solution of problems arising in physics. Through a combination of lectures and exercises, students will gain both theoretical understanding and practical experience with computational techniques.The course covers the following topics, illustrated through simple yet representative numerical algorithms: linear systems of equations; interpolation; numerical differentiation and integration; solution of nonlinear equations; data fitting; eigenvalue problems; ordinary and partial differential equations; and an introduction to the basics of machine learning.In the accompanying exercise sessions, these methods are applied to concrete physical examples. Students will implement the algorithms computationally and visualize the results, fostering an understanding of both the capabilities and limitations of numerical approaches.
Assessment and permitted materials
The assessment consists of a written examination. No written or electronic materials are permitted during the exam. The duration of the examination is approximately 1 hour and 45 minutes. If necessary, further details regarding the assessment format may be announced in due course.
Minimum requirements and assessment criteria
The written examination comprises typically a total of 40–48 points. To receive a positive grade, at least 50% of the total achievable points must be obtained. The grading scheme is as follows:
Grade 1 100.00% - 87.00%
Grade 2 86.99% - 75.00%
Grade 3 74.99% - 63.00%
Grade 4 62.99% - 50.00%
Failed 49.99% - 0.00%
A portion of the total score, typically 6–8 points, is awarded for solving computational exercises based on material covered in the accompanying practical exercise sessions (PUE).
Grade 1 100.00% - 87.00%
Grade 2 86.99% - 75.00%
Grade 3 74.99% - 63.00%
Grade 4 62.99% - 50.00%
Failed 49.99% - 0.00%
A portion of the total score, typically 6–8 points, is awarded for solving computational exercises based on material covered in the accompanying practical exercise sessions (PUE).
Examination topics
The examination covers the material presented in the lectures and exercise sessions, as documented in the lecture notes and presentation slides. In addition to theoretical knowledge, the application of the taught methods to simple, representative problems is required.
Reading list
1) Skriptum und Vortragsfolien @ E-Learning platform Moodle
2) G. Bärwolff, "Numerik für Ingenieure, Physiker und Informatiker", 2016 Springer-Verlag 2nd ed.; DOI 10.1007/978-3-662-48016-8_1 (weiterführend zu allen Kapiteln der Vorlesung mit Beispielen und Programmen, als E-book via u:access verfügbar)
3) A. Quarteroni, F. Saleri und P. Gervasio, "Scientific Computing with MATLAB and Octave", 2010 Springer-Verlag 3rd ed.; ISBN 978-3-642-12429-7
4) P. Deuflhard und A. Hohmann, "Numerical Analysis in Modern Scientific Computing An Introduction", 2003 Springer-Verlag 2nd ed.; ISBN 978-0-387-95410-3
(mathematisch elegant, tiefgehender, enthält kein Material über Differentialgleichungen)
5) P. Deuflhard und A. Hohmann, "Numerische Mathematik 1: Eine algorithmisch orientierte Einführung", 2008 Walter de Gruyter 4th ed.; (1. Band der umfassenden Serie zu Numerischer Mathematik in deutscher Sprache, keine Differentialgleichungen, als E-book via u:access verfügbar)
6) P. Deuflhard und F. Bornemann, "Numerische Mathematik 2: Gewöhnliche Differentialgleichungen", 2013 Walter de Gruyter 4th ed.; (2. Band der umfassenden Serie zu Numerischer Mathematik in deutscher Sprache, als E-book via u:access verfügbar)
7) P. Deuflhard und M. Weiser, "Numerische Mathematik 3: Adaptive Lösung partieller Differentialgleichungen", 2011 Walter de Gruyter; (3. Band der umfassenden Serie zu Numerischer Mathematik in deutscher Sprache, als E-book via u:access verfügbar)
8) Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Available at: http://www.deeplearningbook.org
2) G. Bärwolff, "Numerik für Ingenieure, Physiker und Informatiker", 2016 Springer-Verlag 2nd ed.; DOI 10.1007/978-3-662-48016-8_1 (weiterführend zu allen Kapiteln der Vorlesung mit Beispielen und Programmen, als E-book via u:access verfügbar)
3) A. Quarteroni, F. Saleri und P. Gervasio, "Scientific Computing with MATLAB and Octave", 2010 Springer-Verlag 3rd ed.; ISBN 978-3-642-12429-7
4) P. Deuflhard und A. Hohmann, "Numerical Analysis in Modern Scientific Computing An Introduction", 2003 Springer-Verlag 2nd ed.; ISBN 978-0-387-95410-3
(mathematisch elegant, tiefgehender, enthält kein Material über Differentialgleichungen)
5) P. Deuflhard und A. Hohmann, "Numerische Mathematik 1: Eine algorithmisch orientierte Einführung", 2008 Walter de Gruyter 4th ed.; (1. Band der umfassenden Serie zu Numerischer Mathematik in deutscher Sprache, keine Differentialgleichungen, als E-book via u:access verfügbar)
6) P. Deuflhard und F. Bornemann, "Numerische Mathematik 2: Gewöhnliche Differentialgleichungen", 2013 Walter de Gruyter 4th ed.; (2. Band der umfassenden Serie zu Numerischer Mathematik in deutscher Sprache, als E-book via u:access verfügbar)
7) P. Deuflhard und M. Weiser, "Numerische Mathematik 3: Adaptive Lösung partieller Differentialgleichungen", 2011 Walter de Gruyter; (3. Band der umfassenden Serie zu Numerischer Mathematik in deutscher Sprache, als E-book via u:access verfügbar)
8) Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Available at: http://www.deeplearningbook.org
Association in the course directory
SCICOM, UF MA PHYS 01a, UF MA PHYS 01b
Last modified: Mo 27.07.2026 09:27