The field of flow modeling and simulation is experiencing significant developments, driven by innovative applications of machine learning and mathematical techniques. Researchers are exploring new methods to improve the accuracy and efficiency of flow simulations, including the use of neural surrogates, sparse identification, and stochastic modeling. These advances have the potential to impact various fields, such as aerospace, automotive, and renewable energy. Notably, papers on importance-weighted non-IID sampling and sparse Kalman identification have introduced novel frameworks for estimating expectations and identifying model structures. Another noteworthy paper proposes a neural surrogate model for designing gravitational wave detectors, demonstrating the potential for machine learning to accelerate scientific discovery. Additionally, research on stellarator design using generative AI and sparse-to-field reconstruction via stochastic neural dynamic mode decomposition showcases the growing intersection of machine learning and physics. Some particularly noteworthy papers include: Importance-Weighted Non-IID Sampling for Flow Matching Models, which introduces a novel framework for estimating expectations. Sparse Kalman Identification for Partially Observable Systems via Adaptive Bayesian Learning, which achieves accurate model structure selection with high efficiency. Neural surrogates for designing gravitational wave detectors, which rapidly predicts the quality and feasibility of candidate designs.