The field of cyber-physical systems is witnessing significant developments in verification and control techniques. Researchers are exploring innovative methods to ensure the safety and reliability of these systems, which are critical in various industrial applications. One notable direction is the integration of formal verification techniques, such as model checking, with digital twin models to identify potential errors and improve their behavior. Another area of focus is the development of adaptive control strategies that can handle uncertainties and time-varying constraints, enabling the design of more robust and efficient systems. The use of machine learning and reinforcement learning is also being investigated to improve the autonomy and decision-making capabilities of cyber-physical systems. Noteworthy papers in this area include:

• Contract-based Verification of Digital Twins, which introduces a novel methodology for verifying digital twin models using model checking and contract-based approaches.
• Runtime Safety through Adaptive Shielding, which presents a runtime shielding mechanism for reinforcement learning that ensures probabilistic safety guarantees and optimal policies.
• Adaptive Reinforcement Learning for Unobservable Random Delays, which proposes a framework for handling unobservable and time-varying delays in reinforcement learning. These advances have the potential to significantly impact the development of more reliable, efficient, and autonomous cyber-physical systems.