Scientific Computing I
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2011/2
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|WS 2017/8||SS 2012||WS 2011/2|
IN2005 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Content, Learning Outcome and Preconditions
- steps of the scientific computing simulation pipeline;
- classification of mathematical models (discrete/continuous, deterministic/stochastic, etc.);
- modeling with ordinary differential equations for the example of population growth;
- numerical solution of systems of ordinary differential equations;
- modeling with partial differential equations (PDE) for the example of fluid dynamics;
- numerical discretization methods for partial differential equations (finite elements, time stepping, grid generation);
- algorithms (grid traversal, data storage and access, matrix assembly) for the example of tree-structured grids;
- analysis of methods and results (adequacy and asymptotic behaviour of models; stability, consistency, accuracy, and convergence of numerical methods; sequential and parallel performance of simulation codes).
An outlook will be given on the following topics:
- implementation (architectures, parallel programming, load distribution, domain decomposition, parallel numerical methods);
- visualization for the example of fluid dynamics;
- embedding in larger simulation environments (example fluidstructure interactions);
- interactivity and computational steering.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
Learning and Teaching Methods
- Golub, Ortega: Scientific Computing: An Introduction with Parallel Computing, Academic Press, 1993
- Strang: Computational Science and Engineering, Cambridge University Press, 2007
- Tveito, Winther: Introduction to Partial Differential Equations - A Computational Approach, Springer, 1998
- Boyce, DiPrima: Elementary Differential Equations and Boundary Value Problems, Wiley, 1992 (5th edition)
- Stoer, Bulirsch: Introduction to Numerical Analysis, Springer, 1996
Description of exams and course work
The exam may be repeated at the end of the semester.