|Stochastic Hybrid Systems (SHS) are dynamical models that are employed to characterize the probabilistic evolution of systems with interleaved and interacting continuous and discrete components.
The formal analysis, verification, and optimal control of SHS models represent relevant goals because of their theoretical generality and for their applicability to a wealth of studies in the Sciences and in Engineering.
In this project, we investigate and develop innovative analysis and verification techniques that are directly applicable to general SHS models, while being computationally scalable. The first stage of the study entails mostly analytical work: the project aims at generating results that are both theoretically formal and computationally attractive. It will furthermore develop dedicated software for the analysis of SHS.
The theoretical and computational outcomes will be tested in or applied to a number of studies, in particular to models drawn from Biology and power networks.