The recent developments in the research area demonstrate a significant shift towards leveraging advanced computational methods and innovative mathematical techniques to address complex challenges in power systems and neural dynamics. A notable trend is the adoption of data-driven approaches for model order reduction and load forecasting, which enhance computational efficiency and accuracy. Additionally, there is a growing interest in the application of spectral methods and generative models to simulate and predict system behaviors, particularly in the context of power system oscillations and neural ensemble dynamics. The integration of machine learning with traditional methods is also evident, as seen in the use of time-series foundation models for short-term load prediction, showcasing their potential to outperform classical models without task-specific training. Furthermore, advancements in numerical stability and accuracy in partitioned methods, as well as the exploration of high-order strong stability preserving methods, underscore the importance of robust and efficient algorithms for large-scale simulations. These developments collectively indicate a move towards more sophisticated, data-informed, and computationally efficient solutions in the field.