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

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

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 …

We present SOCKS, a data-driven stochastic optimal control toolbox based in kernel
methods. SOCKS is a collection of data-driven algorithms that compute approximate …

This report presents the results of a friendly competition for formal verification and policy
synthesis of stochastic models. It also introduces new benchmarks within this category, and …

In this paper, we compute finite sample bounds for data-driven approximations of the
solution to stochastic reachability problems. Our approach uses a nonparametric technique …

This report presents the results of a friendly competition for formal verification and policy
synthesis of stochastic models. It also introduces new benchmarks within this category, and …

Hybrid Petri nets with general transitions (HPnGs) are a modeling formalism with discrete,
continuous and random variables, and have successfully been used to model critical …

This report presents the results of a friendly competition for formal verification and policy
synthesis of stochastic models. It also introduces new benchmarks and their properties …