The field of engineering simulations is rapidly advancing with the integration of machine learning techniques. Recent developments have shown that machine learning can be used to accelerate computational fluid dynamics simulations, optimize airfoil geometry, and model nonlinear heterogeneous materials. These advancements have the potential to significantly reduce computational costs and improve the accuracy of simulations. Notably, the use of graph neural networks and physics-informed neural networks has shown promising results in modeling complex systems. The development of new datasets, such as DrivAerStar, is also providing a foundation for further research and applications in industry. Some papers are particularly noteworthy, including the work on AB-UPT for automotive and aerospace applications, which demonstrated strong capabilities in replicating computational fluid dynamics simulations, and the introduction of ViT-Transformer for constitutive modeling of nonlinear heterogeneous materials, which showed remarkable capabilities in capturing long-range dependencies and generalization across microstructures.