The field of cyber-physical systems (CPS) is witnessing significant advancements in safety and control, driven by the need for reliable and efficient systems. Recent research has focused on developing innovative methods for safety verification, control synthesis, and uncertainty representation. A key direction is the integration of data-driven approaches with traditional model-based methods to enhance the accuracy and robustness of safety guarantees. Another important trend is the development of novel control frameworks that can ensure safety, smoothness, and performance in complex CPS applications. Notable papers in this area include:

• Finding Unknown Unknowns using Cyber-Physical System Simulators, which proposes a new goal for testing to discover unknown rare behaviors by examining discrete mode sequences.
• Ensuring Safe and Smooth Control in Safety-Critical Systems via Filtered Control Barrier Functions, which introduces a novel extension of High-Order Control Barrier Functions to produce Lipschitz continuous control inputs.
• Surveying Uncertainty Representation: A Unified Model for Cyber-Physical Systems, which provides a comprehensive review of uncertainty representations and introduces a Conceptual Model of Uncertainty Representations in CPS.
• Control Barrier Function Synthesis for Nonlinear Systems with Dual Relative Degree, which proposes a constructive framework for synthesizing control barrier functions for systems with dual relative degree.
• Data-Driven Safety Verification using Barrier Certificates and Matrix Zonotopes, which leverages matrix zonotopes and barrier certificates to verify system safety directly from noisy data.
• Safety and Security Risk Mitigation in Satellite Missions via Attack-Fault-Defense Trees, which presents a case study on the application of the Attack-Fault-Defense Tree framework to analyze safety, security, and defense mechanisms in satellite missions.
• Convex Computations for Controlled Safety Invariant Sets of Black-box Discrete-time Dynamical Systems, which tackles the problem of identifying controlled safety invariant sets for black-box discrete-time systems.
• Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference, which proposes a novel approach for synthesizing barrier certificates for unknown systems with latent states and polynomial dynamics.
• Graph Analytics for Cyber-Physical System Resilience Quantification, which proposes a methodology based on knowledge graph modeling and graph analytics to quantify the resilience potential of complex systems.
• Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks, which proposes a diffeomorphic function learning framework to learn geometrically-informed Lyapunov functions from data.