The field of physics-informed machine learning is rapidly advancing, with a focus on developing innovative methods for modeling and predicting complex systems. Recent developments have centered around integrating physical laws and constraints into machine learning models, enabling more accurate and interpretable predictions. This has led to significant improvements in areas such as fatigue crack growth prediction, elastoplastic material modeling, and world model simulation. Notably, the use of physics-informed neural networks and conditional neural constitutive laws has shown great promise in capturing complex material behaviors and generalizing across diverse physical scenarios. Furthermore, advances in uncertainty quantification and mechanistic interpretability have enhanced the reliability and trustworthiness of these models. Some noteworthy papers in this area include PC-NCLaws, which proposes a generalizable framework for elastoplastic material modeling, and PhysWorld, which presents a novel framework for learning physics-consistent dynamics models from limited real-world video data. These developments have the potential to transform various fields, from materials science and robotics to climate modeling and beyond. The integration of machine learning and physics-based approaches has also led to improvements in dynamic modeling, enabling the discovery of underlying governing equations and the modeling of high-dimensional systems. The use of neural networks and deep learning algorithms has improved the efficiency and accuracy of dynamic modeling, particularly in cases where data is limited or noisy. Additionally, the development of hierarchical and graph-based methods has enabled the modeling of complex motion hierarchies and the discovery of interpretable motion relationships. The field of computational fluid dynamics and related areas is also witnessing a significant shift towards physically consistent modeling and simulation, with a focus on developing methods that not only provide accurate predictions but also adhere to the underlying physical laws. The integration of diffusion models and preference optimization is being explored to improve the performance of precipitation nowcasting and other applications. Overall, these advances have the potential to significantly improve the accuracy and reliability of machine learning models in complex systems, and are likely to have a major impact on fields such as weather forecasting, autonomous vehicles, and education. The emphasis on calibration and uncertainty estimation is also growing, with a focus on developing more robust and trustworthy models. Researchers are exploring new methods for evaluating and improving model calibration, including the use of equivariant networks, utility-aware calibration, and local calibration techniques. These advancements have the potential to improve model performance in a variety of applications, including safety-critical domains and high-stakes settings such as healthcare. The field of robotics and precision agriculture is also witnessing significant developments, with a focus on improving control systems, enhancing efficiency, and reducing waste. Researchers are exploring innovative methods to optimize robotic control, such as adaptive-speed stirring for accurate pest counting and real-time nonlinear model predictive control for heavy-duty hydraulic manipulators. These advancements have the potential to transform various industries, including agriculture, manufacturing, and logistics.