Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

Abstract Evaluation of scientific contributions can be done in many different ways. For the
various research communities working on the verification of systems (software, hardware, or …

We present an extensive collection of quantitative models to facilitate the development,
comparison, and benchmarking of new verification algorithms and tools. All models have a …

Capturing uncertainty in models of complex dynamical systems is crucial to designing safe
controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of …

Quantitative verification tools compute probabilities, expected rewards, or steady-state
values for formal models of stochastic and timed systems. Exact results often cannot be …

We present SySCoRe, a MATLAB toolbox that synthesizes controllers for stochastic
continuous-state systems to satisfy temporal logic specifications. Starting from a system …

Stochastic hybrid systems (SHS) are a rich mathematical modelling framework capable of
describing complex systems, where uncertainty and hybrid (that is, both continuous and …

We present SReachTools, an open-source MATLAB toolbox for performing stochastic
reachability of linear, potentially time-varying, discrete-time systems that are perturbed by a …

Discrete-time stochastic systems are an essential modeling tool for many engineering
systems. We consider stochastic control systems that are evolving over continuous spaces …

In this paper, we propose a software tool, called AMYTISS, implemented in C++/OpenCL, for
designing correct-by-construction controllers for large-scale discrete-time stochastic …