Safety Verification of Neural Network Controlled Systems
  In this paper, we propose a system-level approach for verifying the safety of
neural network controlled systems, combining a continuous-time physical system
with a discrete-time neural network based controller. We assume a generic model
for the controller that can capture both simple and complex behaviours
involving neural networks. Based on this model, we perform a reachability
analysis that soundly approximates the reachable states of the overall system,
allowing to achieve a formal proof of safety. To this end, we leverage both
validated simulation to approximate the behaviour of the physical system and
abstract interpretation to approximate the behaviour of the controller. We
evaluate the applicability of our approach using a real-world use case.
Moreover, we show that our approach can provide valuable information when the
system cannot be proved totally safe.
