Knowledge:

• Different methods for modeling dynamical processes

• Choose of an appropriate model with respect to the available data

• Technical knowledge to compare different possible modeling approaches and to decide for the most appropriate approach with respect to data and modeling goal

Skills:

• Skills trained cognitive and practically to perform a simulation study correctly,from definition and modeling via software choice and implementation and viamodel identification and model validation up to experiment design, experimentexecution, and result analysis

Competences:

• Communication with experts in various application areas in order to gather informationfor appropriate modeling and for appropriate simulation documentation

• Innovation in formulating dependencies in the model and for the design of communication of appropriate and creative results

Students learn how to perform a simulation study correctly following all necessary steps from data acquisition, (mathematical) modeling, implementation, validation, identification, and result analysis. Different modeling techniques (from differential and difference equations via cellular automata to soft computing-based modeling
techniques and discrete event modeling) are compared and appraised with respect to data availability. Using standard and experimental simulation systems students work on case studies in various application areas (from economics via production to physics and health care).

Registration after the Introduction!

After the registration in TISS you get the access to the "E-Learning Content" in TUWEL.