Correct-by-construction synthesis is a cornerstone of the confluence of formal methods and
control theory towards designing safety-critical systems. Instead of following the time-tested …

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 …

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …

In this paper, we consider the problem of formally verifying the safety of an autonomous
robot equipped with a Neural Network (NN) controller that processes LiDAR images to …

The vulnerability of artificial intelligence (AI) and machine learning (ML) against adversarial
disturbances and attacks significantly restricts their applicability in safety-critical systems …

The Internet of Things (IoT) is a ubiquitous system connecting many different devices-the
things-which can be accessed from the distance. The cyber-physical systems (CPSs) …

Deep neural networks have emerged as a widely used and effective means for tackling
complex, real-world problems. However, a major obstacle in applying them to safety-critical …

In this paper, we consider the problem of output range analysis for feed-forward neural
networks. The current approaches reduce the problem to satisfiability and optimization …

The decision logic for the ACAS X family of aircraft collision avoidance systems is
represented as a large numeric table. Due to storage constraints of certified avionics …

Neural Networks (NN) have been proposed in the past as an effective means for both
modeling and control of systems with very complex dynamics. However, despite the …