The field of predictive modeling and machine learning is moving towards increased integration of numerical simulations, experimental validations, and machine learning techniques. Researchers are exploring the application of digital twins, reduced-order models, and large language models to advance thermal management, time series forecasting, and structural health monitoring. A key trend is the development of novel frameworks that combine the strengths of different approaches, such as the integration of physics-based models with supervised machine learning. Noteworthy papers include: Predictive Digital Twins for Thermal Management Using Machine Learning and Reduced-Order Models, which presents a scalable and interpretable framework for thermal management in automotive systems. ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Data, which introduces a multimodal time series forecasting model that leverages both temporal and textual information for future inference.