Computational Engineering encompasses the acquisition and analysis of data using modern sensor and measurement technologies. Based on this, technical systems and processes are simulated, modelled, and optimised. In addition, methods of machine learning and the integration of artificial intelligence are applied to develop innovative and high-performance technical solutions.
Computational Engineering combines engineering sciences with state-of-the-art data and simulation technologies. This specialisation provides hands-on expertise in data acquisition and analysis, complemented by simulation, optimisation, and machine learning.
The focus is on data-driven solutions for complex technical systems – ranging from virtual prototypes and intelligent production processes to digital twins. Numerical methods, AI-supported approaches, and modern software tools form the methodological foundation.
Application areas are found in mechanical engineering as well as at the interfaces to computer science, electrical engineering, and systems engineering – for example in digital product development, sensor technology, or automated process optimisation. The combination of physics-based models with data science approaches makes Computational Engineering a forward-looking tool in industry and applied research and development.
Contents of the specialisation:
Machine Learning: AI-supported optimisation and data analysis
Numerical Simulation: Structural, fluid, and multiphysics simulation
Digital Twins: Real-time monitoring and virtual prototyping
Programming & Automation: Development of technical tools and processes
Graduates of this specialisation possess a highly sought-after skill profile: analytically strong, digitally proficient, and practice-oriented. They are ideally prepared to drive technological innovation – both at the start of their careers and in further studies.