The objective of this course is to train experts in systems modelling and analysis methodologies. Of course, this will require understanding, to some degree of detail, the mathematical and computational techniques involved. However, this will be done with the aim of shaping good modellers, that know the advantages/disadvantages/risks of the different modelling and analysis methodologies, that are aware of what happens under the hood of a modelling and analysis tool, and that can develop their own tools if needed.
The course will focus on advanced modelling approaches that combine different paradigms and analysis techniques: from ODEs to stochastic models, from simulation to model checking. Case studies from population dynamics, biochemistry, epidemiology, economy and social sciences will be analysed. Moreover, synergistic approaches that combine computational modelling techniques with data-driven methodologies will be outlined.
- Modelling with ODEs: examples
- (Timed and) Hybrid Automata: definition and simulation techniques
- Stochastic simulation methods (Gillespie’s algorithm and its variants)
- Hybrid simulation methods (stochastic/ODEs)
- Rule-based modelling
- Probabilistic/stochastic model checking: principles, applicability and tools
- Statistical model checking
- Process mining (basic notions)