The project investigates quantitative analysis techniques for probabilistic and hybrid systems. These systems are nowadays used to study phenomena occurring in a wide spectrum of application domains, ranging from modern power grids over embedded hard- and software to transportation infrastructure.
The principles of quantitative analysis for these system models have in the recent past been developed by an active research community to which all the project partners belong in prominent roles. However, practical problems remain to achieve scalability of the techniques. The project partners have identified three highly promising angles for attacking scalability: Compositional techniques exploit modularity of a given system for modularity of analysis. Abstraction reduces the complexity of a given system model to the core of what is needed for a focused analysis. Parametrization derives results for families of systems distinguished by model parameters. The project is structured around these three angles, and will discuss how the existing methods and analysis software can be combined together, and how the theories can be exploited in improving the software tools. Our goal is to make the tools usable to analyze real life case studies beyond of what is possible today. The project will derive synergy from the strong complementarity of the respective partner’s expertise and concretize points for future cooperation.