The field of graph neural networks and symbolic regression is rapidly advancing, with a focus on improving the accuracy and efficiency of these methods. Recent developments have seen the introduction of new architectures and techniques, such as the use of physics-informed neural networks and the incorporation of topology optimization. These advancements have the potential to significantly impact a range of applications, from the analysis of complex systems to the design of optimal structures. Notably, the use of graph neural networks for topology optimization has shown promising results, enabling the generation of stress-constrained manufacturable topologies. Additionally, the development of symbolic regression methods has improved the ability to discover equations from data, with applications in fields such as physics and epidemiology. Some noteworthy papers in this area include the introduction of TOFLUX, a topology optimization framework for fluid devices, and the development of EQUATE, a data-efficient fine-tuning framework for symbolic equation discovery. Overall, these advances have the potential to drive significant progress in a range of fields and applications.