Neural network controllers (NNCs) have shown great promise in autonomous and cyber-
physical systems. Despite the various verification approaches for neural networks, the safety …

We introduce ReachNN*, a tool for reachability analysis of neural-network controlled
systems (NNCSs). The theoretical foundation of ReachNN* is the use of Bernstein …

Applying neural networks as controllers in dynamical systems has shown great promises.
However, it is critical yet challenging to verify the safety of such control systems with neural …

Neural Networks (NNs) can provide major empirical performance improvements for robotic
systems, but they also introduce challenges in formally analyzing those systems' safety …

Neural networks (NNs) playing the role of controllers have demonstrated impressive
empirical performance on challenging control problems. However, the potential adoption of …

Autonomous cyber-physical systems (CPS) rely on the correct operation of numerous
components, with state-of-the-art methods relying on machine learning (ML) and artificial …

Deep learning methods can be used to produce control policies, but certifying their safety is
challenging. The resulting networks are nonlinear and often very large. In response to this …

We study the verification problem for closed-loop dynamical systems with neural-network
controllers (NNCS). This problem is commonly reduced to computing the set of reachable …

This paper presents the Neural Network Verification (NNV) software tool, a set-based
verification framework for deep neural networks (DNNs) and learning-enabled cyber …

This paper proposes a new forward reachability analysis approach to verify safety of cyber-
physical systems (CPS) with reinforcement learning controllers. The foundation of our …