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)

This course is aimed for master’s students, in particular for students registered in the master’s program 066 646 Computational Science and Engineering.

Participation in this course is limited to 50 students. Students enrolled in the 066 646 Computational Science and Engineering program are prioritized, followed by students enrolled in the 066 393 Mathematical Modelling in Engineering: Theory, Numerics, Applications program, and finally followed by students enrolled in other master’s programs.

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

C++ and Python (basics)

For self-study, see the FAQs of the master's program ("Will I learn to code?"):
https://www.tuwien.at/en/studies/studies/master-programmes/computational-science-and-engineering/faqs