The field of predictive modeling for industrial and subsurface systems is rapidly evolving, with a focus on developing innovative machine learning and physics-informed approaches to improve accuracy and efficiency. Recent developments have centered on integrating machine learning algorithms with traditional simulation methods to enhance predictive capabilities, particularly in areas such as reservoir pressure management, fluid flow modeling, and predictive maintenance. Notably, the use of differentiable multiphase flow models, recurrent transformer U-Net surrogates, and physics-informed DeepONet has shown significant promise in reducing computational costs while maintaining high accuracy. These advancements have the potential to revolutionize various industries, including petroleum reservoir engineering and industrial pump maintenance, by enabling real-time monitoring, optimized production, and enhanced safety.

Noteworthy papers include: The paper on advancing rail safety through an onboard measurement system of rolling stock wheel flange wear, which achieves an accuracy of 98.2% using dynamic machine learning algorithms and real-time noise reduction via IIR filter. The study on a recurrent transformer U-Net surrogate for flow modeling and data assimilation in subsurface formations with faults, which demonstrates improved accuracy and maintains accuracy for qualitatively different leakage scenarios.