After successful completion of the course, students are able to select and apply fundamental methods of scientific computing and to judge the challenges regarding computing time and implementation effort. Furthermore, the students have solution skills for inter-disciplinery problems, are able to evaluate and analyze computational approaches, and are able to scientifically formulate and extensively analyze compute-intensive problems as well as to develop appropriate approaches. 

• Distributed Parallel Computing
• Molecular Dynamics
• Methods for Ordinary Differential Equations
• Classification and Analyses of Partial Differential Equations
• Time Discretization
• Finite Volume Method
• Finite Element Method

Lectures, exercises and discussion of case studies

• Based on student requests we will focus on on-site teaching in 2022.
• However, should additional COVID-restrictions materialize we will switch entirely to distance learning.
• A parallel hybrid teaching form (on-site and distance at the same time) cannot be offered due to organizatorial reasons.
• The majority of the teaching units is planned on-site; two units will be held by distance learning via zoom (see schedule).
• Important: This course is aimed for master’s students, in particular for students registered in the master’s program 066 646 Computational Science and Engineering.

Exercises hand-in, a minimum threshold is required to be eligible for the final examination at the end of the term

• Important: Limitation to 45 participants.
• The seating limit of the lecture hall defines the participation limitation:
  Although a 100% utilization of the lecture hall is possible, a 50% limitation is still enforced as again a 50% restriction could be implemented at some point. In this case, the on-site teaching can be continued.
• Students registered in the master's program "Computational Science and Engineering" will be prioritized (compulsory subject).

advanced knowledge in C++ and Python required!