The field of solid mechanics is experiencing a significant shift towards the integration of machine learning techniques to improve the accuracy and efficiency of simulations. Researchers are exploring the potential of neural networks and other machine learning approaches to model complex physical phenomena, such as fracture and hyperelasticity. A key direction of research is the development of hybrid methods that combine traditional numerical techniques, like the Finite Element Method, with machine learning algorithms. These approaches aim to leverage the strengths of both fields to create more accurate and efficient simulations. Another area of focus is the creation of robust surrogate models that can approximate complex physical phenomena, reducing the need for computationally expensive simulations. Noteworthy papers include: The paper proposing the Physics-Informed Graph Neural Networks to reconstruct local fields, which achieves significant computational speed-ups compared to traditional Finite Element simulations. The paper introducing a challenging dataset for benchmarking machine learning approaches to fracture modeling, which provides a standardized testbed for evaluating and advancing machine learning in this area.