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

Controllers for dynamical systems that operate in safety-critical settings must account for
stochastic disturbances. Such disturbances are often modeled as process noise in a …

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

Providing non-trivial certificates of safety for non-linear stochastic systems is an important
open problem. One promising solution to address this problem is the use of barrier functions …

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

Neural networks (NNs) are emerging as powerful tools to represent the dynamics of control
systems with complicated physics or black-box components. Due to complexity of NNs …

Stochastic processes are expressive mathematical tools for modeling real-world systems
that are subject to uncertainty. It is hence crucial to be able to formally analyze the behavior …

We consider the problem of computing reach-avoid probabilities for iterative predictions
made with Bayesian neural network (BNN) models. Specifically, we leverage bound …

We present a data-driven framework for strategy synthesis for partially-known switched
stochastic systems. The properties of the system are specified using linear temporal logic …

This paper studies the synthesis of controllers for discrete-time, continuous state stochastic
systems subject to omega-regular specifications using finite-state abstractions. Omega …