The field is moving towards a deeper integration of physical laws and machine learning techniques to improve the accuracy and efficiency of complex systems. This is evident in the development of new methods that incorporate physical constraints into machine learning models, allowing for more accurate predictions and control of systems. One notable trend is the use of physics-informed neural networks, which have been shown to improve the adherence to physical laws and increase the speed of simulations. Another area of research is the development of new control methods that can handle complex systems with multiple interacting components. These advances have the potential to improve the performance and stability of a wide range of systems, from power grids to robotic manipulators. Noteworthy papers include: The paper on All-Electric Heavy-Duty Robotic Manipulator presents a unified framework for the optimization and control of robotic manipulators, using a combination of modeling, optimization, and sensorless control. The paper on Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation provides a comprehensive evaluation of different hybridization strategies for physics-informed neural networks, highlighting their impact on performance and physical compliance.