Scientific Computing with Julia (SCJ)
1. Semester
5 ECTS | 4 SWS
Continuous Assessment (CA)
Compute, Simulate, and Solve Real-World Problems: Learn the Julia programming language and the fundamentals of scientific computing, including numerical methods and the handling of rounding errors. Then develop your own simulations and applications for engineering, healthcare, and business while gaining insight into modern computational methods and their practical implementation.
Contents
- Introcuction into Scientific Computing (i.e.. floating point numbers and rounding problems)
- Introduction into the programming language Julia
- Languages and environments: Julia/VSC/quarto, (Python, jupyter, Matlab etc.)
- Dedicated numerical methods (solving equations, linear equation systems, interpolation, integration, automatic differentiation, pseudo-random-numbers etc.)
- Simulation techniques (differential equations, event-oriented, agent-baed, celluar automata) and optimization
- High-Performance-Computing
- Many examples from different domains (economy, engineering, medicine and more)
- Development of a technical software / simulation as a project in teams
Competencies
Students are able to
- use numerical methods for solving technical problems.
Literature
- Erik Engheim. Julia As a Second Language: General Purpose Programming With a Taste of Data Science, Manning 2023
- Hans-Joachim Bungartz, Stefan Zimmer, Martin Buchholz, Dirk Pflüger. Modeling and Simulation: An application-oriented introduction. Springer, 2014.
- Ulrich Hedtstück. Simulation diskreter Prozesse. Methoden und Anwendungen, Berlin Heidelberg, 2013.
- Uri Wilensky and William Rand. An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. MIT Press, 2015.
- Thomas Huckle and Stefan Schneider. Numerische Methoden: Eine Einführung für Informatiker, Naturwissenschaftler, Ingenieure und Mathematiker. Springer, 2 Edition, 2006.
Lecturer
Recommended Previous Knowledge
-
Interest in software development for scientific and engineering applications, such as AI methods or game programming.
Module Details
| Semester |
1 |
| Lecture Language |
German |
|
Frequency
|
Summer Term
|
| Credit Points (ECTS)
|
5 |
| Course Coordinator |
Prof. Dr. Markus Gumbel |
| Duration |
1 Semester |
| Study Focus |
Medical Data Science (MDS)
Software Engineering (SE)
|
|
Course Achievement
|
None |
|
Prerequisite for exam
|
None |
|
Exam
|
Continuous Assessment (CA) |
Weekly Hours (SWS)
| Lecture |
2 SWS |
| Exercises |
1 SWS |
| Lab |
1 SWS |
| Sum |
4 SWS |
Work Load
| Lecture |
40 h |
| Lab |
30 h |
| Self Study |
40 h |
| Project |
25 h |
| Exam Preparation |
15 h |
| Sum |
150 h |
