After successful completion of the course, students are able to ...

• apply physical, data-driven and hybrid modeling methods.
• apply numerical solution methods for ordinary and partial differential equations.
• clean up measurement data and prepare it for data-driven modeling methods.
• validate models and quantitatively evaluate their accuracy and sensitivity using suitable methods.
• evaluate the applicability of a model for a specific purpose and assess the application limits.

The basis for almost all calculations on industrial energy systems are models that represent the behavior or certain properties of the components. In this course, a wide range of modeling methods is presented - from simple regression models to various data-driven approaches and machine learning methods. Aspects of data preparation and model validation/evaluation will be discussed.

Attendance of the VO offered accompanying to this UE is strongly recommended.

Lectures and exercises

Working on exercises and submission discussions. Further information can be found in the syllabus.

• Fundamentals of thermodynamics, heat transfer
• Basic programming skills in MATLAB