Course F07_Wissenschaftliches Rechnen

---

Responsible: Prof. Dr. Beate Rhein

Course

Meets requirements of following modules(MID)

• in active programs
  • Ma TIN2012 WR

Course Organization

Version created 2012-04-12 VID 1 valid from SS 2015 valid to

Course identifiers Long name F07_Wissenschaftliches Rechnen CID F07_WR_en CEID (exam identifier)

Contact hours per week (SWS) Lecture Exercise (unsplit) Exercise (split) Lab Project Seminar Tutorial(voluntary)

Total contact hours Lecture Exercise (unsplit) Exercise (split) Lab Project Seminar Tutorial (voluntary)

Max. capacity Exercise (unsplit) Exercise (split) Lab Project Seminar

Total effort (hours): 150

Instruction language

• German

Study Level

• Graduate

Prerequisites

Textbooks, Recommended Reading

Instructors

• Prof. Dr. Beate Rhein

Supporting Scientific Staff

Transcipt Entry

en

Assessment

Type
oE	normal case (except on large numbers of assessments: wE

Total effort [hours]
oE

Frequency: 1/year

---

Course components

Lecture/Exercise

Objectives

Contents

• Preliminaries (PFK 2, PFK 4)
• Approximation methods (PFK 5, PFK 6)
  • Meta-modelling methods
  • Regression methods
  • Design of experiments
• Multi-objective optimization (PFK 4, PFK 5, PFK 6)
  • Modeling
  • Pareto front
  • Algorithms
  • Visualization
• Cluster analysis (PFK 5, PFK 6)
  • Partitioning clustering
  • Hierarchical clustering
  • Density-based clustering
  • Cluster evaluation

Acquired Skills

• choose the appropriate method for a given application, implement it cleverly with an efficient numerical algorithm to programs optimized in time and space complexity (PFK 2, PFK 6)
• know approximation methods, select and apply a suitable method for a problem (PFK 5)
• formulate a practical problem as an multi-objective optimzation problem and solve it (PFK 5)
• know methods for cluster analysis, select a suitable algorithm for a problem and apply it (PFK 5)

Additional Component Assessment

Type
fPS

Contribution to course grade
fPS

Frequency:

Lab

Objectives

Acquired Skills

• Use and programming of approximation methods in MATLAB
• Use and programming of multi-objective optimization in MATLAB
• Use and programming of cluster analysis in MATLAB

Operational Competences

• Implement numerical methods efficiently
• Rate algorithms in complexity

Additional Component Assessment

Type
oR	prerequisite to course exam

Contribution to course grade
oR	prerequisite to course exam

Frequency: 1/year