The field of control systems is moving towards a greater emphasis on safety and data-driven approaches. Researchers are developing innovative methods to ensure the safe operation of complex systems, including the use of control barrier functions, neural networks, and physics-informed machine learning. These approaches aim to provide robust guarantees of safety while also improving the performance and efficiency of control systems. Notable papers in this area include:

• Neural Control Barrier Functions from Physics Informed Neural Networks, which introduces a novel class of neural control barrier functions that leverage physics-inspired neural networks to provide scalable and flexible safety guarantees.
• Safe Data-Driven Predictive Control, which presents a safe data-driven predictive control framework that eliminates the need for precise models and reduces computational burdens in nonlinear model predictive control.
• TraCeS: Trajectory Based Credit Assignment From Sparse Safety Feedback, which addresses the challenge of learning safety definitions from sparse safety feedback using a trajectory-based credit assignment approach.