Module Overview

Module A1: "Numerical Methods for Differential Equations" (6 ECTS)

Elective Module A2: "Advanced Topics in Numerical Mathematics" (0-16 ECTS)  

Currently, possible choices are

• Module A2.1: "Numerical Methods for General Types of PDEs"
• Module A2.2: "Constructive Approximation Methods"
• Module A2.3: "Mathematical Control Theory"
• Module A2.4: "Nonlinear Optimization"
• Module A2.5: "Optimization with Differential Equations"

The modules A1, A2 and B1 are the foundation of the master's programme.

Module B1: "Applied Functional Analysis" (8 ECTS)

Elective Module B2: "Modeling and Simulation" (8-16 ECTS)

Currently, modules can be chosen from

• Module B2.1: "Partial Differential Equations and Integral Equations"
• Module B2.2: "Modeling with Differential Equations"
• Module B2.3: "Mathematical Modeling for Climate and Environment"
  
• Module B2.4: "Pattern Recognition"
  
• Module B2.5: "Computational Biophysics"
  
• Module B2.6: "Simulation of Biophysical Systems"
  
• Module B2.7: "Bioinformatics: Molecular Modeling"
  
• Module B2.8: "Foundations of Bioinformatics"
  
• Module B2.9: "Higher Strengths of Materials"
  
• Module B2.10: "Computer Aided Engineering"
  
• Module B2.11: "Model Building and Simulation of Mechanical Systems"
  
• Module B2.12: "Foundations of Data Management"
  

At least 8 ECTS must not stem from the mathematical curriculum.

Module B3: "Industrial Internship" (8 ECTS)

Either a 6 weeks industrial internship or another module from D1.

Module B4: "Modeling and Status Seminar" (8 ECTS)

Each year, a status seminar (winter term) and a one week modeling seminar (summer term) will be held.

• In the status seminar students report their study status and the results of their own research.
• In the modeling seminar students apply their abilities to complex real world problems.

Elective Module C1: "High-Performance Computing" (12 ECTS)

Currently, modules can be chosen from

• Module C1.1: "Algorithms and Data Structures II"
• Module C1.2: "Algorithms and Data Structures III"
• Module C1.3: "Parallel and Distributed Systems I"
• Module C1.4: "Parallel and Distributed Systems II"
• Module C1.5: "High-Performance Computing"
• Module C1.6: "Parallel Algorithms"
  
• Module C1.7: "Programming and Data Analysis in Python"
  

Module C2: Practical course "Parallel Numerical Methods" (2 ECTS)

Elective Module D1: "Complexity Reduction" (16-24 ECTS)

Currently, modules can be chosen from

• Module D1.1: "Efficient treatment of Non-Local Operators"
• Module D1.2: "Fast Methods for Differential and Integral Equations"
• Module D1.3: "Efficient Treatment of Multiscale Problems"
• Module D1.4: "Numerical Methods for Uncertainty Quntification"
• Module D1.5: "High-dimensional Approximation"
• Module D1.6: "Data Analytics"
• Module D1.7: "Complexity Reduction in Control"
• Module D1.8: "Meshfree Methods"
• Module D1.9: "Boundary Element Methods"
  
• Module D1.10: "Optimization Methods in Machine Learning"
  

Elective Module D2: "Special Skills in Scientific Computing" (4 ECTS)

A specialisation module from the research areas listed in D1 or A2.

Participation in seminars of in total 60 hours which improve key qualifications such as presentation techniques, scientific writing, teamwork, literature research and personality development. (2 ECTS)

We encourage students to prepare their master's thesis in coorporation with industry or international experts. Students receive compensation for travel expenses during research stays. (30 ECTS)