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Department of Mechanical Engineering
Summer term lecture

Introduction to Computational Methods (not only) for Engineering

A new introductory course on computational methods (not only) for engineering  is jointly offered by the chairs of Control and Cyberphysical Sytems (RCS)and Reliability Engineering (CRE) under the lead of Profs. Moritz Schulze Darup and Prof. Matthias Faes. The course is especially aimed at students in the early phase of their studies and it aims at providing illustrative insights into indispensable methods (such as computational data analysis, simulation, or optimization and machine learning). These insights are intended to facilitate applications in the course of study (such as for student work) and to prepare for more advanced courses.


Computational methods have become indispensable tools in modern engineering (and R&D in general), revolutionizing the way engineers design, analyze, and optimize complex systems. With the advent of powerful computers and advanced algorithms, engineers can now simulate and model intricate phenomena that were once challenging to comprehend. These methods encompass a wide range of disciplines, including finite element analysis, optimization techniques, and machine learning. Although computational methods offer numerous advantages (not only) to engineering, they necessitate a proficiency in advanced skills from their users.
This short course offers an illuminating introduction to a range of essential computational methods, serving as a valuable resource for students’ studies and paving the way for more advanced courses. Specifically, we will cover the following five topics using concise exercises and small projects (3 h each).

  • Data visualization: Graphs, surface plots, visualizing multidimensional datasets, proper plotting hygiene.
  • Data analysis: Fourier transformation, regression, error bars.
  • Simulation: First encounter with computer-assisted analysis of simple ODEs and PDEs.
  • Optimization: Applying and analyzing linear programming.
  • Machine learning: Supervised and unsupervised using neural networks and clustering.


This short course provides students with essential competences in computational methods for engineering and research. Students will gain proficiency in data visualization, analysis (including Fourier transformation), simulation of differential equations, optimization (linear programming), and machine learning (supervised and unsupervised). For each topic, the students will learn fundamental challenges, strategies for solving them, and suitable computational tools. These competences serve as a foundation for further studies in advanced courses.


The course is designed for an early stage of study and it offers two credit points (2 CP) in the framework of non-disciplinary competences (in German: außerfachliche Kompetenz). Credit is earned by independently completing assignments or a mini-project from the range of applications covered.


Session 1: 08.05.24 Introduction to Matlab / proper coding hygiene (MFA) ??

Session 2: 15.05.24 Data visualisation (MFA)

Session 3: 22.05.24 Data analysis (MSD)

Session 4: 29.05.24 Simulation (MSD)

Session 5: 05.06.24 Optimization (MSD)

Session 6: 12.06.24 Machine learning (MFA)






9:00 - 12:00


MB I - E23/24


Prof. Dr. Matthias GR FaesProf. Dr. Moritz Schulze Darup


Link to Moodle



ECTS Credits: 2 CP  in the framework of non-disciplinary competences (in German: außerfachliche Kompetenz).