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-disciplinary problems, are able to evaluate and analyze computational approaches, and are able to scientifically formulate and extensively analyze compute-intensive problems as well as develop appropriate approaches. 

• Computer Architectures
• Serial Optimization
• Numerical Derivatives and Integrals
• Finite Difference Discretization
• Numerical Linear Algebra
• Random Number Generation and Monte Carlo Methods
• Shared Memory Parallel Computing
• Algorithmic Complexity and Data Structures
• Mesh Generation and Visualiziation
• Software Engineering Principles for Scientific Computing

Lectures (video conference: zoom.us), assignments (group homework) and discussion of case studies (video conference: zoom.us)

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