Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-02-07-M-2

Scientific Computing (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-02-07-M-2 Scientific Computing 4.0 CP (120 h)

Basedata

CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe
Level [2] Bachelor (Fundamentals)
Language [DE] German
Module Manager
Lecturers
Area of study [INF-PFL] Mandatory Modules
Reference course of study [INF-82.79-SG] B.Sc. Computer Science
Livecycle-State [NORM] Active

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V+1U INF-02-07-K-2
Scientific Computing
P 42 h 78 h
U-Schein
- no 4.0 WiSe
  • About [INF-02-07-K-2]: Title: "Scientific Computing"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-02-07-K-2]: The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.

Evaluation of grades

The module is not graded (only study achievements)..


Contents

  • Approaches and problem classes of Scientific Computing
  • Numerical algorithms
    • Numerical stability
    • Fault propagation and condition
  • Solving large linear systems of equations
    • Direct methods (LU, QR, SVD)
    • Iterative methods (CG, BiCG) and preconditioning
  • Numerical approximation
    • Interpolation and approximation
    • Finite differences
    • Quadrature formulas
    • FFT and wavelet transform
  • Non-linear problems
    • Newton method
  • High dimensional problems
    • Monte Carlo simulation
  • Ordinary differential equations
    • Overview & Initial value problems
    • Stability concept
  • Partial differential equations
    • Overview & Examples
    • diffusion and heat equation
  • Tools
    • Scientific programming with Python and C++
    • Outlook: Mainframe Environments
    • Outlook: Parallelisation with OpenMP and MPI
  • Applications and examples in computer science
    • image processing and synthesis
    • Geometric modelling
    • Databases
    • Machine Learning

Competencies / intended learning achievements

Upon successful completion of the module, students will be able to
  • describe the important problem classes and applications in computer science,
  • explain the basic philosophy and problem structure of Scientific Computing (modeling, simulation, optimization, visualization),
  • derive the properties of numerical algorithms,
  • apply important, fundamental numerical methods from linear algebra, analysis, and optimization,
  • identify applications of these methods in computer science,
  • implement simple numerical techniques,
  • use typical tools of scientific computing and to deal exemplarily with corresponding programming interfaces and environments.

Literature

G. H. Golub, J. M. Ortega Scientific Computing and Differential Equations: An Introduction to Numerical Methods. Academic Press, 1st edition, 1991.

Requirements for attendance of the module (informal)

None

Requirements for attendance of the module (formal)

None

References to Module / Module Number [INF-02-07-M-2]

Course of Study Section Choice/Obligation
[INF-82.79-SG] B.Sc. Computer Science [Compulsory Modules] Computer Science Systems [P] Compulsory
Module-Pool Name
[MV-ALLG-2022-MPOOL-6] Wahlpflichtmodule Master allgemein 2022
[MV-ALL-MPOOL-6] Wahlpflichtmodule allgemein
[MV-CE-2022-MPOOL-6] Wahlpflichtmodule M.Sc. Computational Engineering 2022
[MV-CE-MPOOL-6] Wahlpflichtmodule Computational Engineering
[MV-MB-INF-2022-MPOOL-6] Wahlpflichtmodule M.Sc. Maschinenbau mit angewandter Informatik 2022
[MV-MBINFO-MPOOL-6] Wahlpflichtmodule Maschinenbau mit angewandter Informatik