Learning outcomes
After this course, the student will be able to apply a wide range of relevant methods, tools, and processes in the modelling of smart systems. The student will also be able to analyse the different constraints of modelling tools and approaches and apply this in practice.
Content
Introduction: static vs. dynamic models, continuous vs. discrete models, linear vs. nonlinear systems, lumped vs. distributed parameters. Physics-based modelling: mechanical, electrical, fluid, and thermal systems. Data-driven modelling: Artificial Neural networks. Hybrid modelling: Combination of physics-based and data-driven modelling. Digital Twins. Model based design. System and component level modelling tools and languages. System level stability and functionality analysis.
- Teacher: Jani Heikkinen