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

Neural networks have shown great promises in planning, control, and general decision
making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …

Future autonomous systems will employ complex sensing, computation, and communication
components for their perception, planning, control, and coordination, and could operate in …

This report presents the results of a friendly competition for formal verification of continuous
and hybrid systems with artificial intelligence (AI) components. Specifically, machine …

Neural networks have been increasingly applied to control in learning-enabled cyber-
physical systems (LE-CPSs) and demonstrated great promises in improving system …

Future autonomous systems will employ sophisticated machine learning techniques for the
sensing and perception of the surroundings and the making corresponding decisions for …

There is a pressing need for learning controllers to endow systems with properties of safety
and goal-reaching, which are crucial for many safety-critical systems. Reinforcement …

We propose a principled framework that combines adversarial training and provable
robustness verification for training certifiably robust neural networks. We formulate the …

In this article, we present a layer-wise refinement method for neural network output range
analysis. While approaches such as nonlinear programming (NLP) can directly model the …

Neural networks are being increasingly applied to control and decision making for learning-
enabled cyber-physical systems (LE-CPSs). They have shown promising performance …