The field of machine learning and physics-informed neural networks is rapidly advancing, with a focus on developing innovative methods for solving partial differential equations (PDEs) and modeling complex systems. Recent research has explored the use of operator learning, neural ordinary differential equations (ODEs), and physics-informed neural networks to improve the accuracy and efficiency of simulations in various fields, including power systems, ocean dynamics, and weather forecasting. Notably, the integration of differential equations with deep learning has led to the development of new architectures and methodologies that enhance interpretability and generalization capabilities. Furthermore, the application of neural networks to Bayesian inverse problems and the estimation of hidden model parameters has shown promising results. Overall, the field is moving towards the development of more robust, efficient, and interpretable models that can effectively capture complex dynamics and uncertainty. Noteworthy papers include: Operator Learning for Power Systems Simulation, which explores the use of operator learning for simulating power systems, and Principled Operator Learning in Ocean Dynamics, which demonstrates the effectiveness of incorporating temporal Fourier modes in neural operators for high-resolution ocean prediction. Additionally, Temporal Lifting as Latent-Space Regularization for Continuous-Time Flow Models in AI Systems presents a novel method for regularizing near-singular behavior in continuous-time dynamical systems, and Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters proposes a lightweight approach for stability evaluation of grid-tied inverters using neural ODEs.